Audio-Vision Multimodal Review (Paper list)

A curated list of Audio-Vision Multimodal with awesome resources (paper, application, data, review, survey, etc.), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge. Also, there is a MindMap for all papers, which is more intuitive.

We will continue to update this list with newest resources. If you found any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request. If want to collaborate or get in touch, my email is yiyuan.yang@cs.ox.ac.uk.

• Audio-Vision Multimodal Review (Paper list)
  • Audio-vision Machine learning problem - Audio-vision representation learning - Audio-vision saliency detection - Cross-modal transfer learning
  • Audio-vision enhancement - Speech recognition - Speech source enhancement/separation - Speaker verification (active speaker detection) - Sound source separation - Emotion recognition - Action detection - Face super-resolution/reconstruction
  • Cross-modal perception - Cross-modal generation - Video generation - Mono sound generation - Spatial sound generation - Environment generation - Cross-modal retrieval
  • Audio-vision synchronous applications - Audio-vision localization - Sound localization in videos - Audio-vision navigation - Audio-vision Event Localization - Sounding object localization - Audio-vision Parsing - Audio-vision Dialog - Audio-vision correspondence/correlation - Face and Audio Matching - Audio-vision question answering
  • Public dataset
  • Review and survey
  • Diffusion model
  • Citation

Audio-vision Machine learning problem

Audio-vision representation learning

• [ICCV-2017] Look, Listen and Learn

• [CVPR-2018] Seeing voices and hearing faces: Cross-modal biometric matching

• [NeurIPS-2018] Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization

• [CVPR-2019] Deep multimodal clustering for unsupervised  audiovisual  learning

• [ICASSP-2019] Perfect match: Improved cross-modal embeddings for audio-visual synchronisation

• [2020] Self-supervised learning of visual speech features with audiovisual speech enhancement

• [NeurIPS-2020] Learning Representations from Audio-Visual Spatial Alignment

• [NeurIPS-2020] Self-Supervised Learning by Cross-Modal Audio-Video Clustering

• [NeurIPS-2020] Labelling Unlabelled Videos From Scratch With Multi-Modal Self-Supervision

• [ACM MM-2020] Look, listen, and attend: Co-attention network for self-supervised audio-visual representation learning

• [CVPR-2021] Audio-Visual Instance Discrimination with Cross-Modal Agreement

• [CVPR-2021] Robust Audio-Visual Instance Discrimination

• [2021] Unsupervised Sound Localization via Iterative Contrastive Learning

• [ICCV-2021] Multimodal Clustering Networks for Self-Supervised Learning From Unlabeled Videos

• [IJCAI-2021] Speech2talking-face: Inferring and driving a face with synchronized audio-visual representation

• [2021] OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

• [NeurIPS-2021] VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

• [2021] Audio-visual Representation Learning for Anomaly Events Detection in Crowds

• [ICASSP-2022] Audioclip: Extending Clip to Image, Text and Audio

• [CVPR-2022] MERLOT Reserve: Neural Script Knowledge Through Vision and Language and Sound

• [2022] Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning

• [NeurIPS-2022] Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings

• [IEEE TMM-2022] Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations

• [CVPR-2022] Audiovisual Generalised Zero-shot Learning with Cross-modal Attention and Language

• [CVPRW-2022] Multi-task Learning for Human Affect Prediction with Auditory–Visual Synchronized Representation

• [CVPR-2023] Vision Transformers are Parameter-Efficient Audio-Visual Learners

• [CVPR-2022] Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language

• [ECCV-2022] Temporal and cross-modal attention for audio-visual zero-shot learning

• [Int. J. SpeechTechnol.-2022] Complementary models for audio-visual speech classification

• [Appl. Sci.-2022] Data augmentation for audio-visual emotion recognition with an efficient multimodal conditional GAN

• [NeurIPS-2022] u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality

• [NeurIPS-2022] Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

• [IEEE TMM-2022] Audiovisual tracking of concurrent speakers

• [AAAI-2023] Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity

• [ICLR-2023] Contrastive Audio-Visual Masked Autoencoder

• [ICLR-2023] Jointly Learning Visual and Auditory Speech Representations from Raw Data

• [WACV-2023] Audio Representation Learning by Distilling Video as Privileged Information

• [2023] AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations

• [AAAI-2023] Audio-Visual Contrastive Learning with Temporal Self-Supervision

• [CVPR-2023] ImageBind One Embedding Space to Bind Them All

• [CVPR-2021] Spoken moments: Learning joint audio-visual representations from video descriptions

• [2020] Avlnet: Learning audiovisual language representations from instructional videos

• [ECCV-2018] Audio-visual scene analysis with selfsupervised multisensory features

• [MICCAI-2020] Self-Supervised Contrastive Video-Speech Representation Learning for Ultrasound

• [ECCV-2018] Jointly discovering visual objects and spoken words from raw sensory input

• [AAAI-2021] Enhancing Audio-Visual Association with Self-Supervised Curriculum Learning

• [ICLR-2019] Learning hierarchical discrete linguistic units from visually-grounded speech

• [2020] Learning speech representations from raw audio by joint audiovisual self-supervision

• [ICASSP-2020] Visually Guided Self Supervised Learning of Speech Representations

• [ECCV-2020] Leveraging  acoustic  images  for  effective  self-supervised  audio  representation  learning

Audio-vision saliency detection

• [2019] DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction

• [CVPR-2020] STAViS: Spatio-Temporal AudioVisual Saliency Network

• [IEEE TIP-2020] A Multimodal Saliency Model for Videos With High Audio-visual Correspondence

• [IROS-2021] ViNet: Pushing the limits of Visual Modality for Audio-Visuav Saliency Prediction

• [CVPR-2021] From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach

• [ICME-2021] Lavs: A Lightweight Audio-Visual Saliency Prediction Model

• [2022] A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!

• [TOMCCAP-2022] PAV-SOD: A New Task Towards Panoramic Audiovisual Saliency Detection

• [CVPR-2023] CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective

• [CVPR-2023] Self-Supervised Video Forensics by Audio-Visual Anomaly Detection

• [CVPR-2023] CASP-Net: Rethinking Video Saliency Prediction From an Audio-Visual Consistency Perceptual Perspective

• [IJCNN-2023] 3DSEAVNet: 3D-Squeeze-and-Excitation Networks for Audio-Visual Saliency Prediction

• [IEEE TMM-2023] SVGC-AVA: 360-Degree Video Saliency Prediction with Spherical Vector-Based Graph Convolution and Audio-Visual Attention

• [2022] Functional brain networks underlying auditory saliency during naturalistic listening experience

• [ICME-2021] Lavs: A lightweight audio-visual saliency prediction model

• [ICIP-2021] Deep audio-visual fusion neural network for saliency estimation

• [CVPR-2020] STAViS: Spatio–temporal audiovisual saliency network

• [2021] ViNet: Pushing the limits of visual modality for audiovisual saliency prediction

• [2021] Audiovisual saliency prediction via deep learning

• [CVPR-2019] Multi-source weak supervision for saliency detection

Cross-modal transfer learning

• [NeurIPS-2016] SoundNet: Learning Sound Representations from Unlabeled Video

• [ICCV-2019] Self-Supervised Moving Vehicle Tracking With Stereo Sound

• [CVPR-2021] There Is More Than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking With Sound by Distilling Multimodal Knowledge

• [AAAI-2021] Enhanced Audio Tagging via Multi* to Single-Modal Teacher-Student Mutual Learning

• [Interspeech-2021] Knowledge Distillation from Multi-Modality to Single-Modality for Person Verification

• [ICCV-2021] Multimodal Knowledge Expansion

• [CVPR-2021] Distilling Audio-visual Knowledge by Compositional Contrastive Learning

• [2022] Estimating Visual Information From Audio Through Manifold Learning

• [DCASE-2021] Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy

• [Interspeech-2021] Audiovisual transfer learning for audio tagging and sound event detection

• [2023] Revisiting Pre-training in Audio-Visual Learning

• [IJCNN-2023] A Generative Approach to Audio-Visual Generalized Zero-Shot Learning: Combining Contrastive and Discriminative Techniques

• [ICCV-2023] Audio-Visual Class-Incremental Learning

• [ICCV-2023] Hyperbolic Audio-visual Zero-shot Learning

• [CVPR-2023] Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

• [ICCV-2023] Class-Incremental Grouping Network for Continual Audio-Visual Learning

Audio-vision enhancement

Speech recognition

• [Applied Intelligence-2015] Audio-visual Speech Recognition Using Deep Learning

• [CVPR-2016] Temporal Multimodal Learning in Audiovisual Speech Recognition

• [AVSP-2017] End-To-End Audiovisual Fusion With LSTMs

• [IEEE TPAMI-2018] Deep Audio-visual Speech Recognition

• [ICASSP-2018] End-to-End Audiovisual Speech Recognition

• [2019] Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

• [ICASSP-2019] Modality attention for end-to-end audio-visual speech recognition

• [CVPR-2020] Discriminative Multi-Modality Speech Recognition

• [ICASSP0-2021] End-to-end audiovisual speech recognition with conformers

• [IEEE TNNLS-2022] Multimodal Sparse Transformer Network for Audio-visual Speech Recognition

• [Interspeech-2022] Robust Self-Supervised Audio-visual Speech Recognition

• [2022] Bayesian Neural Network Language Modeling for Speech Recognition

• [Interspeech-2022] Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition

• [MLSP-2022] Rethinking Audio-visual Synchronization for Active Speaker Detection

• [NeurIPS-2022] A Single Self-Supervised Model for Many Speech Modalities Enables Zero-Shot Modality Transfer

• [ITOEC-2022] FSMS: An Enhanced Polynomial Sampling Fusion Method for Audio-Visual Speech Recognition

• [IJCNN-2022] Continuous Phoneme Recognition based on Audio-Visual Modality Fusion

• [ICIP-2022] Learning Contextually Fused Audio-Visual Representations For Audio-Visual Speech Recognition

• [ICASSP-2023] Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation

• [CVPR-2022] Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations

• [AAAI-2022] Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading

• [AAAI-2023] Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning

• [WACV-2023] Audio-Visual Efficient Conformer for Robust Speech Recognition

• [2023] Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition

• [2023] Multimodal Speech Recognition for Language-Guided Embodied Agents

• [2023] MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation

• [ICASSP-2023] The NPU-ASLP System for Audio-Visual Speech Recognition in MISP 2022 Challenge

• [CVPR-2023] Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring

• [ICASSP-2023] Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels

• [CVPR-2023] AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR

• [CVPR-2023] SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision

• [ICASSP-2023] Multi-Temporal Lip-Audio Memory for Visual Speech Recognition

• [ICASSP-2023] On the Role of LIP Articulation in Visual Speech Perception

• [ICASSP-2023] Practice of the Conformer Enhanced Audio-Visual Hubert on Mandarin and English

• [ICASSP-2023] Robust Audio-Visual ASR with Unified Cross-Modal Attention

• [IJCAI-2023] Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition

• [Interspeech-2023] Prompting the Hidden Talent of Web-Scale Speech Models for Zero-Shot Task Generalization

• [Interspeech-2023] Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning

• [ACL-2023] AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

• [ACL-2023] Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition

• [ACL-2023] MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition

• [IJCNN-2023] Exploiting Deep Learning for Sentence-Level Lipreading

• [IJCNN-2023] GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

• [ICME-2023] Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder

• [ICME-2023] Multi-Scale Hybrid Fusion Network for Mandarin Audio-Visual Speech Recognition

• [2023] A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition

• [EUSIPCO-2022] Visual Speech Recognition in a Driver Assistance System

• [Interspeech-2023] DAVIS: Driver’s Audio-Visual Speech Recognition

• [ICASSP-2020] Audio-Visual Recognition of Overlapped Speech for the LRS2 Dataset

• [ICCV-2023] SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision

• [CVPR-2021] Learning from the master: Distilling cross-modal advanced knowledge for lip reading

• [AAAI-2022] Crossmodal mutual learning for audio-visual speech recognition and manipulation

• [AAAI-2020] Hearing lips: Improving lip reading by distilling speech recognizers

• [ACMMM-2020] A lip sync expert is all you need for speech to lip generation in the wild

• [ACMMM-2021] Cross-modal selfsupervised learning for lip reading: When contrastive learning meets adversarial training

• [ICASSP-2020] Asr is all you need: Crossmodal distillation for lip reading

• [2021] Sub-word level lip reading with visual attention

• [2019] Improving audio-visual speech recognition performance with crossmodal student-teacher training

• [ICASSP-2021] End-to-end audio-visual speech recognition with conformers

Speech source enhancement/separation

• [IEEE/ACM Trans. Audio, Speech, Lang. Proces-2018] Using visual speech information in masking methods for audio speaker separation

• [Interspeech-2018] DNN driven speaker independent audio-visual mask estimation for speech separation

• [ICASSP-2018] Seeing through noise: Visually driven speaker separation and enhancement

• [ACM Trans on Graphics-2018] Looking  to listen at the cocktail party: A speaker-independent audiovisual model for speech separation

• [Interspeech-2018] Visual Speech Enhancement

• [Interspeech-2018] The Conversation: Deep Audio-Visual Speech Enhancement

• [IEEE TETCI-2018] Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

• [ICASSP-2018] Seeing Through Noise: Visually Driven Speaker Separation And Enhancement

• [ICASSP-2019] Face landmark-based speaker-independent  audio-visual  speech  enhancement  in  multi-talker  environments

• [IEEE/ACM Trans. Audio, Speech, Lang. Process-2019] Audio-visual deep clustering for speech separation

• [2019] Mixture of inference networks for VAE-based audio-visual speech enhancement

• [GlobalSIP-2019] Visually Assisted Time-Domain Speech Enhancement

• [ICASSP-2019] On Training Targets and Objective Functions for Deep-learning-based Audio-visual Speech Enhancement

• [InterSpeech-2019] Multimodal SpeakerBeam: Single Channel Target Speech Extraction with Audio-Visual Speaker Clues

• [ICASSP-2019] Effects of Lombard reflex on the performance of deep-learning-based audio-visual speech enhancement systems

• [Interspeech-2019] My Lips Are Concealed: Audio-Visual Speech Enhancement Through Obstructions

• [ICASSP-2020] A robust audio-visual speech enhancement model

• [2020] Facefilter: Audio-Visual Speech Separation Using Still Images

• [ICASSP-2020] A visual-pilot deep fusion for target speech separation in multitalker noisy environment

• [ICASSP-2020] Deep audio-visual speech separation with attention mechanism

• [ICASSP-2020] AV(SE)2: Audio-visual squeeze-excite speech enhancement

• [Interspeech-2020] Lite audio-visual speech enhancement

• [ICASSP-2020] Robust Unsupervised Audio-Visual Speech Enhancement Using a Mixture of Variational Autoencoders

• [CVPR-2021] Looking Into Your Speech: Learning Cross-Modal Affinity for Audio-Visual Speech Separation

• [ISCAS-2021] Audio-Visual Target Speaker Enhancement on Multi-Talker Environment using Event-Driven Cameras

• [ICASSP-2022] The Impact of Removing Head Movements on Audio-Visual Speech Enhancement

• [2022] Dual-path Attention is All You Need for Audio-Visual Speech Extraction

• [ICASSP-2022] Audio-visual multi-channel speech separation, dereverberation and recognition

• [2022] Audio-visual speech separation based on joint feature representation with cross-modal attention

• [CVPR-2022] Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis

• [IEEE MMSP-2022] As We Speak: Real-Time Visually Guided Speaker Separation and Localization

• [IEEE HEALTHCOM-2022] A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids

• [CVPR-2022] Reading to Listen at the Cocktail Party: Multi-Modal Speech Separation

• [WACV-2023] BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

• [SLT-2023] AVSE Challenge: Audio-Visual Speech Enhancement Challenge

• [ICLR-2023] Filter-Recovery Network for Multi-Speaker Audio-Visual Speech Separation

• [WACV-2023] Unsupervised Audio-Visual Lecture Segmentation

• [ISCSLP-2022] Multi-Task Joint Learning for Embedding Aware Audio-Visual Speech Enhancement

• [ICASSP-2023] Real-Time Audio-Visual End-to-End Speech Enhancement

• [ICASSP-2023] Efficient Intelligibility Evaluation Using Keyword Spotting: A Study on Audio-Visual Speech Enhancement

• [ICASSP-2023] Incorporating Visual Information Reconstruction into Progressive Learning for Optimizing audio-visual Speech Enhancement

• [ICASSP-2023] Audio-Visual Speech Enhancement with a Deep Kalman Filter Generative Model

• [ICASSP-2023] A Multi-Scale Feature Aggregation Based Lightweight Network for Audio-Visual Speech Enhancement

• [ICASSP-2023] Egocentric Audio-Visual Noise Suppression

• [ICASSP-2023] Dual-Path Cross-Modal Attention for Better Audio-Visual Speech Extraction

• [ICASSP-2023] On the Role of Visual Context in Enriching Music Representations

• [ICASSP-2023] LA-VOCE: LOW-SNR Audio-Visual Speech Enhancement Using Neural Vocoders

• [ICASSP-2023] Learning Audio-Visual Dereverberation

• [Interspeech-2023] Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation

• [Interspeech-2023] Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model

• [ITG-2023] Audio-Visual Speech Enhancement with Score-Based Generative Models

• [Interspeech-2023] Speech inpainting: Context-based speech synthesis guided by video

• [EUSIPCO-2023] Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction

• [IEEE/ACM TASLP-2023] Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition

• [ICCV-2023] AdVerb: Visually Guided Audio Dereverberation

• [ICCV-2023] Position-Aware Audio-Visual Separation for Spatial Audio

• [ICCV-2023] Leveraging Foundation Models for Unsupervised Audio Visual Segmentation

• [2021] Visualvoice: Audio-visual speech separation with crossmodal consistency

• [2021] A cappella: Audio-visual singing voice separation

• [2020] Facefilter: Audiovisual speech separation using still images

• [2020] Deep variational generative models for audio-visual speech separation

Speaker verification (active speaker detection)

• [MTA-2016] Audio-visual Speaker Diarization Using Fisher Linear Semi-discriminant Analysis

• [ICASSP-2018] Audio-visual Person Recognition in Multimedia Data From the Iarpa Janus Program

• [ICASSP-2019] Noise-tolerant Audio-visual Online Person Verification Using an Attention-based Neural Network Fusion

• [Interspeech-2019] Who Said That?: Audio-visual Speaker Diarisation Of Real-World Meetings

• [ICASSP-2020] Self-Supervised Learning for Audio-visual Speaker Diarization

• [ICASSP-2020] The sound of my voice: Speaker representation loss for target voice separation

• [ICASSP-2021] A Multi-View Approach to Audio-visual Speaker Verification

• [IEEE/ACM TASLP-2021] Audio-visual Deep Neural Network for Robust Person Verification

• [ICDIP 2022] End-To-End Audiovisual Feature Fusion for Active Speaker Detection

• [EUVIP-2022] Active Speaker Recognition using Cross Attention Audio-Video Fusion

• [2022] Audio-Visual Activity Guided Cross-Modal Identity Association for Active Speaker Detection

• [SLT-2023] Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection

• [ICAI-2023] Speaker Recognition in Realistic Scenario Using Multimodal Data

• [CVPR-2023] A Light Weight Model for Active Speaker Detection

• [ICASSP-2023] The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition

• [ICASSP-2023] ImagineNet: Target Speaker Extraction with Intermittent Visual Cue Through Embedding Inpainting

• [ICASSP-2023] Speaker Recognition with Two-Step Multi-Modal Deep Cleansing

• [ICASSP-2023] Audio-Visual Speaker Diarization in the Framework of Multi-User Human-Robot Interaction

• [ICASSP-2023] Cross-Modal Audio-Visual Co-Learning for Text-Independent Speaker Verification

• [ICASSP-2023] Multi-Speaker End-to-End Multi-Modal Speaker Diarization System for the MISP 2022 Challenge

• [ICASSP-2023] Av-Sepformer: Cross-Attention Sepformer for Audio-Visual Target Speaker Extraction

• [ICASSP-2023] The WHU-Alibaba Audio-Visual Speaker Diarization System for the MISP 2022 Challenge

• [ICASSP-2023] Self-Supervised Audio-Visual Speaker Representation with Co-Meta Learning

• [Interspeech-2023] Target Active Speaker Detection with Audio-visual Cues

• [Interspeech-2023] CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition

• [Interspeech-2023] Rethinking the visual cues in audio-visual speaker extraction

• [ICAI-2023] Speaker Recognition in Realistic Scenario Using Multimodal Data

• [ACL-2023] OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment

• [ICASSP-2023] AV-SepFormer: Cross-Attention SepFormer for Audio-Visual Target Speaker Extraction

• [Interspeech-2023] PIAVE: A Pose-Invariant Audio-Visual Speaker Extraction Network

• [NIPS-2018] Transfer learning from speaker verification to multispeaker text-to-speech synthesis

Sound source separation

• [IEEE Signal Process. Lett-2018] Listen and look: Audio-visual matching assisted speech source separation

• [ECCV-2018] Learning to Separate Object Sounds by Watching Unlabeled Video

• [IEEE Signal Processing Letters-2018] Listen  and  look: Audio–visual matching assisted speech source separation

• [ECCV-2018] The Sound of Pixels

• [ICASSP-2019] Self-supervised Audio-visual Co-segmentation

• [ICCV-2019] The Sound of Motions

• [ICCV-2019] Recursive Visual Sound Separation Using Minus-Plus Net

• [ICCV-2019] Co-Separating Sounds of Visual Objects

• [ACCV-2020] Visually Guided Sound Source Separation using Cascaded Opponent Filter Network

• [2020] Conditioned source separation for music instrument performances

• [CVPR-2020] Music Gesture for Visual Sound Separation

• [ICCV-2021] Visual Scene Graphs for Audio Source Separation

• [CVPR-2021] Cyclic Co-Learning of Sounding Object Visual Grounding and Sound Separation

• [ECCV-2022] AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation

• [ICIP-2022] Visual Sound Source Separation with Partial Supervision Learning

• [NeurIPS-2022] Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation

• [ICLR-2023] CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos

• [CVPR-2023] Language-Guided Audio-Visual Source Separation via Trimodal Consistency

• [CVPR-2023] iQuery: Instruments As Queries for Audio-Visual Sound Separation

• [2020] Into the wild with audioscope: Unsupervised audio-visual separation of on-screen sounds

• [ECCV-2020] Self-supervised Learning of Audio-Visual Objects from Video

Emotion recognition

• [EMNLP-2017] Tensor Fusion Network for Multimodal Sentiment Analysis

• [AAAI-2018] Multi-attention Recurrent Network for Human Communication Comprehension

• [AAAI-2018] Memory Fusion Network for Multi-view Sequential Learning

• [NAACL-2018] Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos

• [EMNLP-2018] Contextual Inter-modal Attention for Multi-modal Sentiment Analysis

• [IEEE Transactions on Affective Computing-2019] An  active learning  paradigm  for  online  audio-visual  emotion  recognition

• [ACL-2019] Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model

• [ACL-2020] Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis

• [ACL-2020] A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis

• [ACL-2020] Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation

• [CVPR-2021] Progressive Modality Reinforcement for Human Multimodal Emotion Recognition From Unaligned Multimodal Sequences

• [IEEE TAFFC-2021] Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations

• [IEEE SLT-2021] Detecting expressions with multimodal transformers

• [CVPR-2022] M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation

• [CCC-2022] A Multimodal Emotion Perception Model based on Context-Aware Decision-Level Fusion

• [IJCNN-2022] Sense-aware BERT and Multi-task Fine-tuning for Multimodal Sentiment Analysis

• [Appl. Sci.-2022] Data augmentation for audio-visual emotion recognition with an efficient multimodal conditional GAN

• [IEEE/ACM TASLP-2022] EmoInt-Trans: A Multimodal Transformer for Identifying Emotions and Intents in Social Conversations

• [ICPR-2022] Self-attention fusion for audiovisual emotion recognition with incomplete data

• [IEEE TAFFC-2023] Audio-Visual Emotion Recognition With Preference Learning Based on Intended and Multi-Modal Perceived Labels

• [IEEE T-BIOM-2023] Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention

• [ICASSP-2023] Adapted Multimodal Bert with Layer-Wise Fusion for Sentiment Analysis

• [ICASSP-2023] Recursive Joint Attention for Audio-Visual Fusion in Regression Based Emotion Recognition

• [IEEE/ACM TASLP-2023] Exploring Semantic Relations for Social Media Sentiment Analysis

• [CVPR-2023] Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

• [ACM MM-2023] Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023

• [IJCNN-2019] Deep fusion: An attention guided factorized bilinear pooling for audio-video emotion recognition

• [2021] Does visual self-supervision improve learning of speech representations for emotion recognition

• [CVPR-2021] Audiodriven emotional video portraits

Action detection

• [IJCNN-2016] Exploring Multimodal Video Representation For Action Recognition

• [CVPR-2018] The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary

• [ICCV-2019] EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition

• [ICCV-2019] SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition

• [ICCV-2019] Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference

• [CVPR-2020] Listen to Look: Action Recognition by Previewing Audio

• [2020] Audiovisual SlowFast Networks for Video Recognition

• [ICCV-2021] AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

• [2021] Cross-Domain First Person Audio-Visual Action Recognition through Relative Norm Alignment

• [WACV-2022] Domain Generalization Through Audio-Visual Relative Norm Alignment in First Person Action Recognition

• [CVPR-2022] Audio-Adaptive Activity Recognition Across Video Domains

• [WACV-2022] MM-ViT: Multi-Modal Video Transformer for Compressed Video Action Recognition

• [CVPR-2022] Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos

• [2022] Noise-Tolerant Learning for Audio-Visual Action Recognition

• [ICLR-2023] Exploring Temporally Dynamic Data Augmentation for Video Recognition

• [ICASSP-2023] Epic-Sounds: A Large-scale Dataset of Actions That Sound

• [ICASSP-2023] AV-TAD: Audio-Visual Temporal Action Detection With Transformer

• [ICCV-2023] Audio-Visual Glance Network for Efficient Video Recognition

• [IEEE TMM-2023] Audio-Visual Contrastive and Consistency Learning for Semi-Supervised Action Recognition

• [Sensors-2023] Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices

Face super-resolution/reconstruction

• [CVPR-2020] Learning to Have an Ear for Face Super-Resolution

• [IEEE TCSVT-2021] Appearance Matters, So Does Audio: Revealing the Hidden Face via Cross-Modality Transfer

• [CVPR-2022] Deep Video Inpainting Guided by Audio-Visual Self-Supervision

• [CVPR-2022] Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

• [WACV-2023] Audio-Visual Face Reenactment

• [ICASSP-2023] Hearing and Seeing Abnormality: Self-Supervised Audio-Visual Mutual Learning for Deepfake Detection

• [CVPR-2023] AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction

• [CVPR-2023] Parametric Implicit Face Representation for Audio-Driven Facial Reenactment

• [CVPR-2023] CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior

• [2021] One-shot talking face generation from single-speaker audio-visual correlation learning

• [CVPR-2021] Flow-guided one-shot talking face generation with a high-resolution audio-visual dataset

• [NIPS-2019] Face  reconstruction  from voice  using  generative  adversarial  networks

Cross-modal perception

Cross-modal generation

Video generation

• Generate face

  • [ACM TOG-2017] Synthesizing Obama: learning lip sync from audio

  • [ECCV-2018] Lip Movements Generation at a Glance

  • [IJCV-2019] You Said That?: Synthesising Talking Faces from Audio

  • [AAAI-2019] Talking face generation by adversarially disentangled audio-visual representation

  • [ICCV-2019] Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

  • [IJCAI-2020] Arbitrary  talking  face  generation  via  attentional  audio-visual coherence  learning

  • [IJCV-2020] Realistic Speech-Driven Facial Animation with GANs

  • [IJCV-2020] GANimation: One-Shot Anatomically Consistent Facial Animation

  • [ACM TOG-2020] Makelttalk: Speaker-Aware Talking-Head Animation

  • [CVPR-2020] FReeNet: Multi-Identity Face Reenactment

  • [ECCV-2020] Neural Voice Puppetry: Audio-driven Facial Reenactment

  • [CVPR-2020] Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images

  • [ECCV-2020] MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation

  • [AAAI-2021] Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation

  • [CVPR-2021] Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation

  • [CVPR-2021] Audio-Driven Emotional Video Portraits

  • [AAAI-2022] One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning

  • [TVCG-2022] Generating talking face with controllable eye movements by disentangled blinking feature

  • [AAAI-2022] SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory

  • [CVPR-2022] FaceFormer: Speech-Driven 3D Facial Animation with Transformers

  • [CVPR-2023] Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert

  • [ICASSP-2023] Free-View Expressive Talking Head Video Editing

  • [ICASSP-2023] Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model

  • [ICASSP-2023] Naturalistic Head Motion Generation from Speech

  • [ICASSP-2023] Audio-Visual Inpainting: Reconstructing Missing Visual Information with Sound

  • [CVPR-2023] Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

  • [CVPR-2023] SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

  • [ACM MM-2023] Hierarchical Semantic Perceptual Listener Head Video Generation: A High-performance Pipeline

  • [CVPR-2023] LipFormer: High-fidelity and Generalizable Talking Face Generation with A Pre-learned Facial Codebook

  • [IEEE TMM-2022] Audio-driven talking face video generation with dynamic convolution kernels

• Generate gesture

  • [IVA-2018] Evaluation of Speech-to-Gesture Generation Using Bi-Directional LSTM Network

  • [IVA-2019] Analyzing Input and Output Representations for Speech-Driven Gesture Generation

  • [CVPR-2019] Learning Individual Styles of Conversational Gesture

  • [ICMI-2019] To React or not to React: End-to-End Visual Pose Forecasting for Personalized Avatar during Dyadic Conversations,

  • [EUROGRAPHICS-2020] Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows

  • [ICMI-2020] Gesticulator: A Framework For Semantically-Aware Speech-Driven Gesture Generation

  • [2020] Style Transfer for Co-Speech Gesture Animation: A Multi-Speaker Conditional-Mixture Approach

  • [ACM TOG-2020] Speech Gesture Generation From The Trimodal Context Of Text, Audio, And Speaker Identity

  • [CVPR-2022] SEEG: Semantic Energized Co-Speech Gesture Generation

  • [IEEE TNNLS-2022] VAG: A Uniform Model for Cross-Modal Visual-Audio Mutual Generation

  • [CVPR-2023] Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation

• Generate dance

  • [ACM MM-2018] Dance with Melody: An LSTM-autoencoder Approach to Music-oriented Dance Synthesis

  • [CVPR-2018] Audio to Body Dynamics

  • [NeurIPS-2019] Dancing to Music

  • [ICLR-2021] Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning

  • [ICCV-2021] AI Choreographer: Music Conditioned 3D Dance Generation With AIST++

  • [ICASSP-2022] Genre-Conditioned Long-Term 3D Dance Generation Driven by Music

  • [CVPR-2022] Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory

  • [CVPR-2023] MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

  • [IEEE TMM-2023] Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization

  • [ICCV-2023] Listen and Move: Improving GANs Coherency in Agnostic Sound-to-Video Generation

• Other

  • [CVPR-2023] Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment

  • [2021] Sound-guided semantic image manipulation

  • [2022] Learning visual styles from audio-visual associations

  • [ICCV-2023] Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment

  • [2021] Speech2video: Cross-modal distillation for speech to video generation

  • [2018] CMCGAN: A uniform framework  for  cross-modal  visual-audio  mutual  generation

  • [IEEE/ACM Transactions on Audio, Speech, and Language Processing-2021] Generating  images  from  spoken  descriptions

Mono sound generation

• Speech

  • [ICASSP-2017] Vid2speech: Speech Reconstruction From Silent Video

  • [ICCV-2017] Improved Speech Reconstruction From Silent Video

  • [ICASSP-2018] Lip2Audspec: Speech Reconstruction from Silent Lip Movements Video

  • [ACM MM-2018] Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed

  • [Interspeech-2019] Video-Driven Speech Reconstruction using Generative Adversarial Networks

  • [Interspeech-2019] Hush-Hush Speak: Speech Reconstruction Using Silent Videos

  • [ICASSP-2021] Learning Audio-Visual Correlations From Variational Cross-Modal Generation

  • [IEEE TCYB-2022] End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks

  • [ICPR-2022] Learning Speaker-specific Lip-to-Speech Generation

  • [ICASSP-2023] Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech

  • [CVPR-2023] ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration

  • [ICCV-2023] DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding

  • [ICCV-2023] Let There Be Sound: Reconstructing High Quality Speech from Silent Videos

  • [CVPR-2019] Speech2Face:  Learning  the face behind a voice

• Music

  • [IEEE TMM-2015] Real-Time Piano Music Transcription Based on Computer Vision

  • [ACM MM-2017] Deep Cross-Modal Audio-Visual Generation

  • [NeurIPS-2020] Audeo: Audio Generation for a Silent Performance Video

  • [ECCV-2020] Foley Music: Learning to Generate Music from Videos

  • [ICASSP-2020] Sight to Sound: An End-to-End Approach for Visual Piano Transcription

  • [2020] Multi-Instrumentalist Net: Unsupervised Generation of Music from Body Movements

  • [ICASSP-2021] Collaborative Learning to Generate Audio-Video Jointly

  • [ACM-2021] Video Background Music Generation with Controllable Music Transformer

  • [2022] Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation

  • [CVPR-2023] Conditional Generation of Audio from Video via Foley Analogies

  • [ICML-2023] Long-Term Rhythmic Video Soundtracker

• Natural Sound

  • [CVPR-2016] Visually Indicated Sounds

  • [CVPR-2018] Visual to Sound: Generating Natural Sound for Videos in the Wild

  • [IEEE TIP-2020] Generating Visually Aligned Sound From Videos

  • [BMVC-2021] Taming Visually Guided Sound Generation

  • [IEEE TCSVT-2022] Towards an End-to-End Visual-to-Raw-Audio Generation With GAN

  • [ICASSP-2023] I Hear Your True Colors: Image Guided Audio Generation

  • [CVPR-2023] Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos

Spatial sound generation

• [ACM TOG-2018] Scene-aware audio for 360° videos

• [NeurIPS-2018] Self-Supervised Generation of Spatial Audio for 360° Video

• [CVPR-2019] 2.5D Visual Sound

• [ICIP-2019] Self-Supervised Audio Spatialization with Correspondence Classifier

• [ECCV-2020] Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation

• [CVPR-2021] Visually Informed Binaural Audio Generation without Binaural Audios

• [AAAI-2021] Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation

• [TOG-2021] Binaural Audio Generation via Multi-task Learning

• [WACV-2022] Beyond Mono to Binaural: Generating Binaural Audio From Mono Audio With Depth and Cross Modal Attention

• [CVPR-2023] Novel-View Acoustic Synthesis

• [ICCV-2023] Separating Invisible Sounds Toward Universal Audio-Visual Scene-Aware Sound Separation

Environment generation

• [ICRA-2020] BatVision: Learning to See 3D Spatial Layout with Two Ears

• [ECCV-2020] VISUALECHOES: Spatial Image Representation Learning Through Echolocation

• [CVPR-2021] Beyond Image to Depth: Improving Depth Prediction Using Echoes

• [ICASSP-2022] Co-Attention-Guided Bilinear Model for Echo-Based Depth Estimation

• [NeurIPS-2022] Learning Neural Acoustic Fields

• [NeurIPS-2022] Few-Shot Audio-Visual Learning of Environment Acoustics

Cross-modal retrieval

• [2017] Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint

• [ICCV-2017] Image2song: Song Retrieval via Bridging Image Content and Lyric Words

• [CVPR-2018] Seeing voices and hearing faces: Cross-modal biometric matching

• [ECCV-2018] Cross-modal Embeddings for Video and Audio Retrieval

• [ISM-2018] Audio-Visual Embedding for Cross-Modal Music Video Retrieval through Supervised Deep CCA

• [TOMCCAP-2020] Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval

• [IEEE TGRS-2020] Deep Cross-Modal Image–Voice Retrieval in Remote Sensing

• [2021] Learning Explicit and Implicit Latent Common Spaces for Audio-Visual Cross-Modal Retrieval

• [ICCV-2021] Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion

• [IJCAI-2022] Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast

• [IEEE ISM-2022] Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval

• [IEEE SMC-2022] Graph Network based Approaches for Multi-modal Movie Recommendation System

• [CVPR-2022] Visual Acoustic Matching

• [2021] Learning explicit and implicit latent common spaces for audio-visual cross-modal retrieval

• [2021] Variational autoencoder with cca for audio-visual cross-modal retrieval.

• [TMM-2021] Adversarial-metric learning for audio-visual cross-modal matching

Audio-vision synchronous applications

Audio-vision localization

Sound localization in videos

• [ECCV-2018] Objects that Sound

• [CVPR-2018] Learning to localize sound source in visual scenes

• [ECCV-2018] Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

• [ECCV-2018] The Sound of Pixels

• [ICASSP-2019] Self-supervised Audio-visual Co-segmentation

• [ICCV-2019] The Sound of Motions

• [CVPR-2019] Deep Multimodal Clustering for Unsupervised Audiovisual Learning

• [CVPR-2021] Localizing Visual Sounds the Hard Way

• [IEEE TPAMI-2021] Class-aware Sounding Objects Localization via Audiovisual Correspondence

• [IEEE TPAMI-2021] Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications

• [CVPR-2022] Mix and Localize: Localizing Sound Sources in Mixtures

• [ECCV-2022] Audio-Visual Segmentation

• [2022] Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization

• [ACM MM-2022] Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation

• [CVPR-2022] Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes

• [CVPR-2022] Self-supervised object detection from audio-visual correspondence

• [EUSIPCO-2022] Visually Assisted Self-supervised Audio Speaker Localization and Tracking

• [ICASSP-2023] MarginNCE: Robust Sound Localization with a Negative Margin

• [IEEE TMM-2022] Cross modal video representations for weakly supervised active speaker localization

• [NeurIPS-2022] A Closer Look at Weakly-Supervised Audio-Visual Source Localization

• [AAAI-2022] Visual Sound Localization in the Wild by Cross-Modal Interference Erasing

• [ECCV-2022] Sound Localization by Self-Supervised Time Delay Estimation

• [IEEE/ACM TASLP-2023] Audio-Visual Cross-Attention Network for Robotic Speaker Tracking

• [WACV-2023] Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization

• [WACV-2023] Exploiting Visual Context Semantics for Sound Source Localization

• [2023] Audio-Visual Segmentation with Semantics

• [CVPR-2023] Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning

• [CVPR-2023] Egocentric Audio-Visual Object Localization

• [CVPR-2023] Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning

• [CVPR-2023] Audio-Visual Grouping Network for Sound Localization from Mixtures

• [ICASSP-2023] Flowgrad: Using Motion for Visual Sound Source Localization

• [ACM MM-2023] Audio-visual segmentation, sound localization, semantic-aware sounding objects localization

• [ACM MM-2023] Induction Network: Audio-Visual Modality Gap-Bridging for Self-Supervised Sound Source Localization

• [ACM MM-2023] Audio-Visual Spatial Integration and Recursive Attention for Robust Sound Source Localization

• [IJCAI-2023] Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation

• [IROS-2020] Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling

Audio-vision navigation

• [ECCV-2020] SoundSpaces: Audio-Visual Navigation in 3D Environments

• [ICRA-2020] Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

• [ICLR-2021] Learning to Set Waypoints for Audio-Visual Navigation

• [CVPR-2021] Semantic Audio-Visual Navigation

• [ICCV-2021] Move2Hear: Active Audio-Visual Source Separation

• [2022] Sound Adversarial Audio-Visual Navigation

• [CVPR-2022] Towards Generalisable Audio Representations for Audio-Visual Navigation

• [NeurIPS-2022] SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

• [NeurIPS-2022] AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments

• [BMVC-2022] Pay Self-Attention to Audio-Visual Navigation

• [CVPR-2022] Finding Fallen Objects Via Asynchronous Audio-Visual Integration

• [CVPR-2022] ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

• [IEEE RAL-2023] Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds

• [2023] Audio Visual Language Maps for Robot Navigation

• [ICCV-2023] Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation

Audio-vision Event Localization

• [CVPR-2018] Weakly  supervised  representation  learning for unsynchronized audio-visual events

• [ECCV-2018] Audio-visual Event Localization in Unconstrained Videos

• [ICASSP-2019] Dual-modality Seq2Seq Network for Audio-visual Event Localization

• [ICCV-2019] Dual Attention Matching for Audio-Visual Event Localization

• [2020] Crossmodal learning  for  audio-visual  speech  event  localization

• [AAAI-2020] Cross-Modal Attention Network for Temporal Inconsistent Audio-Visual Event Localization

• [ACCV-2020] Audiovisual Transformer with Instance Attention for Audio-Visual Event Localization

• [WACV-2021] Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention

• [CVPR-2021] Positive Sample Propagation along the Audio-Visual Event Line

• [AIKE-2021] Audio-Visual Event Localization based on Cross-Modal Interacting Guidance

• [TMM-2021] Audio-Visual Event Localization by Learning Spatial and Semantic Co-attention

• [CVPR-2022] Cross-Modal Background Suppression for Audio-Visual Event Localization

• [ICASSP-2022] Bi-Directional Modality Fusion Network For Audio-Visual Event Localization

• [ICSIP-2022] Audio-Visual Event and Sound Source Localization Based on Spatial-Channel Feature Fusion

• [IJCNN-2022] Look longer to see better: Audio-visual event localization by exploiting long-term correlation

• [EUSIPCO-2022] Audio Visual Graph Attention Networks for Event Detection in Sports Video

• [IEEE TPAMI-2022] Contrastive Positive Sample Propagation along the Audio-Visual Event Line

• [IEEE TPAMI-2022] Semantic and Relation Modulation for Audio-Visual Event Localization

• [WACV-2023] AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization

• [WACV-2023] Event-Specific Audio-Visual Fusion Layers: A Simple and New Perspective on Video Understanding

• [ICASSP-2023] A dataset for Audio-Visual Sound Event Detection in Movies

• [CVPR-2023] Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline

• [CVPR-2023] Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

• [ICASSP-2023] Collaborative Audio-Visual Event Localization Based on Sequential Decision and Cross-Modal Consistency

• [CVPR-2023] Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception

• [IJCNN-2023] Specialty may be better: A decoupling multi-modal fusion network for Audio-visual event localization

• [AAAI-2023] Furnishing Sound Event Detection with Language Model Abilities

• [ICCV-2023] Prompting Segmentation with Sound is Generalizable Audio-Visual Source Localizer

Sounding object localization

• [CVPR-2018] Learning to localize sound source in visual scenes

• [IROS-2021] AcousticFusion: Fusing sound source localization to visual SLAM in dynamic environments

• [ITPAMI-2022] Classaware sounding objects localization via audiovisual correspondence

• [PR-2021]Multimodal fusion for indoor sound source localization

• [CVPR-2021] Localizing Visual Sounds the Hard Way

Audio-vision Parsing

• [ECCV-2020] Unified Multisensory Perception: Weakly-Supervised Audio-Visual Video Parsing

• [CVPR-2021] Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing

• [NeurIPS-2021] Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing

• [2022] Investigating Modality Bias in Audio Visual Video Parsing

• [ICASSP-2022] Distributed Audio-Visual Parsing Based On Multimodal Transformer and Deep Joint Source Channel Coding

• [ECCV-2022] Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

• [NeurIPS-2022] Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing

• [2023] Improving Audio-Visual Video Parsing with Pseudo Visual Labels

• [ICASSP-2023] CM-CS: Cross-Modal Common-Specific Feature Learning For Audio-Visual Video Parsing

• [2023] Towards Long Form Audio-visual Video Understanding

• [CVPR-2023] Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio* Visual Event Perception

Audio-vision Dialog

• [CVPR-2019] Audio Visual Scene-Aware Dialog

• [Interspeech-2019] Joint Student-Teacher Learning for Audio-Visual Scene-Aware Dialog

• [ICASSP-2019] End-to-end Audio Visual Scene-aware Dialog Using Multimodal Attention-based Video Features

• [CVPR-2019] A Simple Baseline for Audio-Visual Scene-Aware Dialog

• [CVPR-2019] Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog

• [2020] TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog

• [AAAI-2021] Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

• [2021] VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs

• [ICASSP-2022] Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning

• [WACV-2022] QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog

• [TACL-2022] Learning English with Peppa Pig

• [2022] End-to-End Multimodal Representation Learning for Video Dialog

• [AAAI-2022] Audio Visual Scene-Aware Dialog Generation with Transformer-based Video Representations

• [IEEE/ACM TASLP-2023] DialogMCF: Multimodal Context Flow for Audio Visual Scene-Aware Dialog

Audio-vision correspondence/correlation

• [ICASSP-2019] Learning affective correspondence between music and image

• [2020] Themes informed audio-visual correspondence learning

• [CVPR-2021] Audio-Visual Instance Discrimination with Cross-Modal Agreement

• [Computer Vision and Image Understanding* 2023] Unsupervised sound localization via iterative contrastive learning

• [2021] Vatt: Transformers for multimodal selfsupervised learning from raw video, audio and text.

• [NeurIPS-2020] Self-supervised multimodal versatile networks

• [Neurocomputing-2020] Audio–visual domain adaptation using conditional semi-supervised generative adversarial networks

• [2021] Cross-modal attention consistency for video-audio unsupervised learning

• [ICCV-2023] Bi-directional Image-Speech Retrieval Through Geometric Consistency

Face and Audio Matching

• [2017] Putting a face to the voice: Fusing audio and visual signals across a video to determine speakers

• [CVPR-2018] Seeing voices and hearing faces: Cross-modal biometric matching

• [ECCV-2018] Learnable pins: Crossmodal embeddings for person identity

• [ICLR-2019] Disjoint mapping network for cross-modal matching of voices and faces

• [ICME-2019] A novel distance learning for elastic cross-modal audio-visual matching

• [ECCV-2018] Crossmodal embeddings for video and audio retrieval

Audio-vision question answering

• [ICCV-2021] Pano-AVQA: Grounded Audio-Visual Question Answering on 360deg Videos

• [CVPR-2022] Learning To Answer Questions in Dynamic Audio-Visual Scenarios

• [NeurIPS-2022] Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

• [ACM MM-2023] Progressive Spatio-temporal Perception for Audio-Visual Question Answering

Public dataset

• LRW, LRS2 and LRS3 (Speech-related, speaker-related,face generation-related tasks)

• VoxCeleb, VoxCeleb2 (Speech-related, speaker-related,face generation-related tasks)

• AVA-ActiveSpeaker (Speech-related task, speaker-related task)

• Kinetics-400 (Action recognition)

• EPIC-KITCHENS (Action recognition)

• CMU-MOSI (Emotion recognition)

• CMU-MOSEI (Emotion recognition)

• VGGSound (Action recognition, sound localization)

• AudioSet (Action recognition, sound sepearation)

• Greatest Hits ()Sound generation

• MUSIC (Sound seperation, sound localization)

• FAIR-Play (Spatial sound generation)

• YT-ALL (Spatial sound generation)

• Replica (Depth estimation)

• AIST++ (Dance generation)

• TED (Gesture generation)

• SumMe (Saliency detection)

• AVE (Event localization)

• LLP (Event parsing)

• SoundSpaces (Audio-visual navigation)

• AVSD (Audio-visual dialog)

• Pano-AVQA (Audio-visual question answering)

• MUSIC-AVQA (Audio-visual question answering)

• AVSBench (Audio-visual segmentation, sound localization)

• HDTF (Face generation)

• MEAD (Face generation)

• RAVDESS (Face generation)

• GRID (Face generation)

• TCD-TIMIT (Speech recognition)

• CN-CVS (Continuous Visual to Speech Synthesis)

• SoundNet (sound representation from unlabeled video)

• ACAV100M (large-scale data of high audio-visual)

• SEWA-DB (emotion  and  sentiment  research in the wild)

Review and survey

• [Image Vis. Comput.-2014] A Review of Recent Advances in Visual Speech Decoding

• [2015] Audiovisual Fusion: Challenges and New Approaches

• [2017] Multimedia Datasets for Anomaly Detection: A Review

• [2017] Multimodal Machine Learning: A Survey and Taxonomy

• [2018] A Survey of Multi-View Representation Learning

• [Int. J. Adv. Robot. Syst-2020] Audiovisual Speech Recognition: A Review and Forecast

• [2021] A Survey on Audio Synthesis and Audio-Visual Multimodal Processing

• [2021] An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

• [2021] Deep Audio-visual Learning: A Survey

• [2022] Learning in Audio-visual Context: A Review, Analysis, and New Perspective

• [2022] A review of deep learning techniques in audio event recognition (AER) applications

• [2022] Audio self-supervised learning: A survey

• [2022] Deep Learning for Visual Speech Analysis: A Survey

• [2022] Recent Advances and Challenges in Deep Audio-Visual Correlation Learning

• [2023] A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition

Diffusion model

• [CVPR-2023] Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation

• [CVPR-2023] MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

• [CVPR-2023] Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos

• [ICCV-2023] DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding

• [ICASSP-2023] Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech

Citation

If you find this repository helpful for your work, please kindly cite:

@misc{yang2023Audiovisionmodal,
  title={Awesome-AudioVision-Multimodal},
  author={Yang, Yiyuan},
  journal = {GitHub repository},
  year={2023}
}