Robust detection of moving vehicles is a critical task for any autonomously operating outdoor robot or self-driving vehicle. Most modern approaches for solving this task rely on training image-based detectors using large-scale vehicle detection datasets such as nuScenes or the Waymo Open Dataset. Providing manual annotations is an expensive and laborious exercise that does not scale well in practice. To tackle this problem, we propose a self-supervised approach that leverages audio-visual cues to detect moving vehicles in videos. Our approach employs contrastive learning for localizing vehicles in images from corresponding pairs of images and recorded audio. In extensive experiments carried out with a real-world dataset, we demonstrate that our approach provides accurate detections of moving vehicles and does not require manual annotations. We furthermore show that our model can be used as a teacher to supervise an audio-only detection model. This student model is invariant to illumination changes and thus effectively bridges the domain gap inherent to models leveraging exclusively vision as the predominant modality.
Audio-Visual Segmentation via Depth-Guided Collaborative Modeling
School of Intelligent Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Recent work on audio-visual navigation targets a single static sound in noise-free audio environments and struggles to generalize to unheard sounds. We introduce the novel dynamic audio-visual navigation benchmark in which an embodied AI agent must catch a moving sound source in an unmapped environment in the presence of distractors and noisy sounds. We propose an end-to-end reinforcement learning approach that relies on a multi-modal architecture that fuses the spatial audio-visual information from a binaural audio signal and spatial occupancy maps to encode the features needed to learn a rob…
Audio-visual navigation combines sight and hearing to navigate to a sound-emitting source in an unmapped environment. While recent approaches have demonstrated the benefits of audio input to detect and find the goal, they focus on clean and static sound sources and struggle to generalize to unheard sounds. In this work, we propose the novel dynamic audio-visual navigation benchmark which requires catching a moving sound source in an environment with noisy and distracting sounds, posing a range of new challenges. We introduce a reinforcement learning approach that learns a robust navigation pol…
Audio-Guided Visual Perception for Audio-Visual Navigation
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing（丝绸之路多语种认知计算国际联合实验室）；Tsinghua University（清华大学）；Tianjin University of Technology（天津工业大学）
Video caption refers to generating a descriptive sentence for a specific short video clip automatically, which has achieved remarkable success recently. However, most of the existing methods focus more on visual information while ignoring the synchronized audio cues. We propose three multimodal deep fusion strategies to maximize the benefits of visual-audio resonance information. The first one explores the impact on cross-modalities feature fusion from low to high order. The second establishes the visual-audio short-term dependency by sharing weights of corresponding front-end networks. The th…
UT Austin（德克萨斯大学奥斯汀分校）；Facebook AI Research（Facebook人工智能研究院）
Recent work on audio-visual navigation assumes a constantly-sounding target and restricts the role of audio to signaling the target's position. We introduce semantic audio-visual navigation, where objects in the environment make sounds consistent with their semantic meaning (e.g., toilet flushing, door creaking) and acoustic events are sporadic or short in duration. We propose a transformer-based model to tackle this new semantic AudioGoal task, incorporating an inferred goal descriptor that captures both spatial and semantic properties of the target. Our model's persistent multimodal memory e…
Can Textual Semantics Mitigate Sounding Object Segmentation Preference?
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Edinburgh（爱丁堡大学）
This paper studies audio-visual deep saliency prediction. It introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed ``DAVE" in conjunction with our efforts towards building an Audio-Visual Eye-tracking corpus named ``AVE". Despite existing a strong relation between auditory and visual cues for guiding gaze during perception, video saliency models only consider visual cues and neglect the auditory information that is ubiquitous in dynamic scenes. Here, we investigate the applicability of audio cues in conjunction with visual ones in pre…
We introduce a novel self-supervised pretext task for learning representations from audio-visual content. Prior work on audio-visual representation learning leverages correspondences at the video level. Approaches based on audio-visual correspondence (AVC) predict whether audio and video clips originate from the same or different video instances. Audio-visual temporal synchronization (AVTS) further discriminates negative pairs originated from the same video instance but at different moments in time. While these approaches learn high-quality representations for downstream tasks such as action r…
We present an introspection of an audiovisual speech enhancement model. In particular, we focus on interpreting how a neural audiovisual speech enhancement model uses visual cues to improve the quality of the target speech signal. We show that visual cues provide not only high-level information about speech activity, i.e., speech/silence, but also fine-grained visual information about the place of articulation. One byproduct of this finding is that the learned visual embeddings can be used as features for other visual speech applications. We demonstrate the effectiveness of the learned visual …
Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment
The University of Queensland（昆士兰大学）；NetEase Fuxi AI Lab（网易伏羲人工智能实验室）；Matrix Verse AI（矩阵 Verse 人工智能公司）；CSIRO Data61（联邦科学与工业研究组织 Data61 部门）；Macau University of Science and Technology（澳门科技大学）
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake d…
The Dynamic Saliency Prediction (DSP) task simulates the human selective attention mechanism to perceive the dynamic scene, which is significant and imperative in many vision tasks. Most of existing methods only consider visual cues, while neglect the accompanied audio information, which can provide complementary information for the scene understanding. In fact, there exists a strong relation between auditory and visual cues, and humans generally perceive the surrounding scene by collaboratively sensing these cues. Motivated by this, an audio-visual collaborative representation learning method…
Key Laboratory of Machine Perception, Peking University, Shenzhen Graduate School（北京大学深圳研究生院机器感知重点实验室）；Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）
Multi-modal fusion is proven to be an effective method to improve the accuracy and robustness of speaker tracking, especially in complex scenarios. However, how to combine the heterogeneous information and exploit the complementarity of multi-modal signals remains a challenging issue. In this paper, we propose a novel Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities. Specifically, a novel acoustic map based on spatial-temporal Global Coherence Field (stGCF) is first constructed for heterogeneous signal fusion, which employs a camera model to map …
Dense video captioning aims to localize and describe important events in untrimmed videos. Existing methods mainly tackle this task by exploiting only visual features, while completely neglecting the audio track. Only a few prior works have utilized both modalities, yet they show poor results or demonstrate the importance on a dataset with a specific domain. In this paper, we introduce Bi-modal Transformer which generalizes the Transformer architecture for a bi-modal input. We show the effectiveness of the proposed model with audio and visual modalities on the dense video captioning task, yet …
Human speech processing is inherently multimodal, where visual cues (lip movements) help to better understand the speech in noise. Lip-reading driven speech enhancement significantly outperforms benchmark audio-only approaches at low signal-to-noise ratios (SNRs). However, at high SNRs or low levels of background noise, visual cues become fairly less effective for speech enhancement. Therefore, a more optimal, context-aware audio-visual (AV) system is required, that contextually utilises both visual and noisy audio features and effectively accounts for different noisy conditions. In this paper…
Indian Institute of Technology Ropar（印度理工学院罗巴尔分校）；University of Canberra（堪培拉大学）
While personality traits have been extensively modeled as behavioral constructs, we model \textbf{\textit{job hirability}} as a \emph{personality construct}. On the {\emph{First Impressions Candidate Screening}} (FICS) dataset, we examine relationships among personality and hirability measures. Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP). We also examine the efficacy of a tw…
Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University and Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing（人工智能研究院、深圳MSU-BIT大学及粤港澳大湾区情感智能与普适计算联合实验室）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Since facial actions such as lip movements contain significant information about speech content, it is not surprising that audio-visual speech enhancement methods are more accurate than their audio-only counterparts. Yet, state-of-the-art approaches still struggle to generate clean, realistic speech without noise artifacts and unnatural distortions in challenging acoustic environments. In this paper, we propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech…
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction
School of Computer Science, Nanjing University of Information Science and Technology（信息科学与技术南京大学计算机科学学院）；Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology（大气环境与设备技术江苏省协同创新中心）；School of Computer Science and Informatics, Cardiff University（计算机科学与信息学院卡斯尔大学）；Faculty of Information Technology and Communication Sciences, Tampere University（信息科技与通讯科学学院坦佩雷大学）
Spherical Vision Transformers for Audio-Visual Saliency Prediction in 360-Degree Videos
Department of Computer Science and Engineering, Koç University（计算机科学与工程系，科克大学）；Department of Psychology, Boğaziçi University（心理学系，博多伊大学）；National Institute of Advanced Industrial Science and Technology (AIST), Intelligent Platforms Research Institute（国家先进工业科学与技术研究院（AIST），智能平台研究机构）；Department of Psychology, Boğaziçi University University（心理学系，博多伊大学）；Department of Computer Engineering, Hacettepe University（计算机工程系，哈切塞特佩大学）；KUIS AI Center（KUIS人工智能中心）
University of Oulu（奥卢大学）；Northwestern Polytechnical University（西北工业大学）；École de technologie supérieure（高等技术学院）
Visual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verification task, visual kinship verification is particularly difficult with low-quality found Internet data. Due to uncontrolled variations in background, pose, facial expression, blur, illumination and occlusion, state-of-the-art methods fail to provide high level of recognition accuracy. As with many other visual recognition tasks, kinship verification may benefit from combining visual and audio signals. However, voice-ba…
Ref-AVS: Refer and Segment Objects in Audio-Visual Scenes
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
MoMuSE: Momentum Multi-modal Target Speaker Extraction for Real-time Scenarios with Impaired Visual Cues
The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Hong Kong University of Science and Technology（香港科技大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
The integration of information across multiple modalities and across time is a promising way to enhance the emotion recognition performance of affective systems. Much previous work has focused on instantaneous emotion recognition. The 2018 One-Minute Gradual-Emotion Recognition (OMG-Emotion) challenge, which was held in conjunction with the IEEE World Congress on Computational Intelligence, encouraged participants to address long-term emotion recognition by integrating cues from multiple modalities, including facial expression, audio and language. Intuitively, a multi-modal inference network s…
CineSRD: Leveraging Visual, Acoustic, and Linguistic Cues for Open-World Visual Media Speaker Diarization
Hujing Digital Media and Entertainment Group（华景数字媒体与娱乐集团）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部门）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
SMART: Shot-Aware Multimodal Video Moment Retrieval with Audio-Enhanced MLLM
Department of Computer Science, University at Albany - SUNY（University at Albany - SUNY 计算机科学系）；School of Software & Microelectronics, Peking University（北京大学软件与微电子学院）；Nanjing University（南京大学）；Xiamen University（厦门大学）；Department of Mathematics and Statistics, University at Albany - SUNY（University at Albany - SUNY 数学与统计学系）
MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；IBM Research AI（IBM研究人工智能部门）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
Humans are able to localize objects in the environment using both visual and auditory cues, integrating information from multiple modalities into a common reference frame. We introduce a system that can leverage unlabeled audio-visual data to learn to localize objects (moving vehicles) in a visual reference frame, purely using stereo sound at inference time. Since it is labor-intensive to manually annotate the correspondences between audio and object bounding boxes, we achieve this goal by using the co-occurrence of visual and audio streams in unlabeled videos as a form of self-supervision, wi…
