With the rapid expansion of e-commerce, more consumers have become accustomed to making purchases via livestreaming. Accurately identifying the products being sold by salespeople, i.e., livestreaming product retrieval (LPR), poses a fundamental and daunting challenge. The LPR task encompasses three primary dilemmas in real-world scenarios: 1) the recognition of intended products from distractor products present in the background; 2) the video-image heterogeneity that the appearance of products showcased in live streams often deviates substantially from standardized product images in stores; 3) there are numerous confusing products with subtle visual nuances in the shop. To tackle these challenges, we propose the Spatiotemporal Graphing Multi-modal Network (SGMN). First, we employ a text-guided attention mechanism that leverages the spoken content of salespeople to guide the model to focus toward intended products, emphasizing their salience over cluttered background products. Second, a long-range spatiotemporal graph network is further designed to achieve both instance-level interaction and frame-level matching, solving the misalignment caused by video-image heterogeneity. Third, we propose a multi-modal hard example mining, assisting the model in distinguishing highly similar products with fine-grained features across the video-image-text domain. Through extensive quantitative and qualitative experiments, we demonstrate the superior performance of our proposed SGMN model, surpassing the state-of-the-art methods by a substantial margin. The code is available at https://github.com/Huxiaowan/SGMN.
Macau University of Science and Technology（澳门科技大学）；Alibaba Group（阿里巴巴集团）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognition methods have achieved promising performance through the tightly coupled multi-modal spatiotemporal representation, they still suffer from (i) optimization difficulty under small data setting due to the tightly spatiotemporal-entangled modeling;(ii) information redundancy as it usually contains lots of marginal information that is weakly relevant to classification; and (iii) low interaction between multi-modal spati…
Video-Level Language-Driven Video-Based Visible-Infrared Person Re-Identification
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Chongqing Institute for Brain and Intelligence（重庆脑科学与智能技术研究院）；Guangyang Bay Laboratory（广阳湾实验室）
Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period. New methods for time-to-event prediction have been developed by extending the cox-proportional hazards model with neural networks. In this paper, to explore the feasibility of these methods when applied to deep learning with echocardiography videos, we utilize the Stanford EchoNet-Dynamic dataset with over 10,000 echocardiograms, and generate simulated survival datasets …
Huazhong University of Science and Technology（华中科技大学）；SenseTime Research（商汤科技研究院）；Chinese University of Hong Kong（香港中文大学）
Camera and 3D LiDAR sensors have become indispensable devices in modern autonomous driving vehicles, where the camera provides the fine-grained texture, color information in 2D space and LiDAR captures more precise and farther-away distance measurements of the surrounding environments. The complementary information from these two sensors makes the two-modality fusion be a desired option. However, two major issues of the fusion between camera and LiDAR hinder its performance, \ie, how to effectively fuse these two modalities and how to precisely align them (suffering from the weak spatiotempora…
In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which r…
SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；University of Nottingham（诺丁汉大学）；School of AI, Shenzhen University（深圳大学人工智能学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）
This paper presents a pure transformer-based approach, dubbed the Multi-Modal Video Transformer (MM-ViT), for video action recognition. Different from other schemes which solely utilize the decoded RGB frames, MM-ViT operates exclusively in the compressed video domain and exploits all readily available modalities, i.e., I-frames, motion vectors, residuals and audio waveform. In order to handle the large number of spatiotemporal tokens extracted from multiple modalities, we develop several scalable model variants which factorize self-attention across the space, time and modality dimensions. In …
Current video representations heavily rely on learning from manually annotated video datasets which are time-consuming and expensive to acquire. We observe videos are naturally accompanied by abundant text information such as YouTube titles and Instagram captions. In this paper, we leverage this visual-textual connection to learn spatiotemporal features in an efficient weakly-supervised manner. We present a general cross-modal pair discrimination (CPD) framework to capture this correlation between a video and its associated text. Specifically, we adopt noise-contrastive estimation to tackle th…
Convolutional operations have two limitations: (1) do not explicitly model where to focus as the same filter is applied to all the positions, and (2) are unsuitable for modeling long-range dependencies as they only operate on a small neighborhood. While both limitations can be alleviated by attention operations, many design choices remain to be determined to use attention, especially when applying attention to videos. Towards a principled way of applying attention to videos, we address the task of spatiotemporal attention cell search. We propose a novel search space for spatiotemporal attentio…
CrossVideoMAE: Self-Supervised Image-Video Representation Learning with Masked Autoencoders
University of Moratuwa（莫拉图瓦大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Auckland（奥克兰大学）；Weizmann Institute of Science（魏茨曼科学研究所）
CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection
Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；Guangdong OPPO Mobile Communications Co., Ltd.（广东OPPO移动通信有限公司）；City University of Hong Kong（香港城市大学）
Depth can provide useful geographical cues for salient object detection (SOD), and has been proven helpful in recent RGB-D SOD methods. However, existing video salient object detection (VSOD) methods only utilize spatiotemporal information and seldom exploit depth information for detection. In this paper, we propose a depth-cooperated trimodal network, called DCTNet for VSOD, which is a pioneering work to incorporate depth information to assist VSOD. To this end, we first generate depth from RGB frames, and then propose an approach to treat the three modalities unequally. Specifically, a multi…
Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis
Philips（飞利浦）；Columbia University Vagelos College of Physicians and Surgeons（哥伦比亚大学瓦格洛斯内科医生与外科医生学院）；MedStar Washington Hospital Center（MedStar华盛顿医院中心）；Yale University School of Medicine（耶鲁大学医学院）
M^3Detection: Multi-Frame Multi-Level Feature Fusion for Multi-Modal 3D Object Detection with Camera and 4D Imaging Radar
Radar Technology Research Institute, School of Information and Electronics, Beijing Institute of Technology（雷达技术研究所，信息电子学院，北京理工大学）；Key Laboratory of Electronic and Information Technology in Satellite Navigation, Ministry of Education（卫星导航电子信息技术重点实验室，教育部）；School of Computer Science, Beijing Institute of Technology（计算机学院，北京理工大学）；Beijing Racobit Electronic Information Technology Co., Ltd.（北京Racobit电子信息技术有限公司）
Sun Yat-sen University（中山大学）；Tencent（腾讯）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
In this technical report, we introduce our solution to human-centric spatio-temporal video grounding task. We propose a concise and effective framework named STVGFormer, which models spatiotemporal visual-linguistic dependencies with a static branch and a dynamic branch. The static branch performs cross-modal understanding in a single frame and learns to localize the target object spatially according to intra-frame visual cues like object appearances. The dynamic branch performs cross-modal understanding across multiple frames. It learns to predict the starting and ending time of the target mo…
Learning semantical dynamics and spatiotemporal collaboration for human pose estimation in video
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Istituto Italiano di Tecnologia（意大利技术研究院）；Universita‘ degli Studi di Genova（热那亚大学）；Universita‘ di Verona（维罗纳大学）
Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) dataset could be accurately designed to include a variety of sensory inputs, it is often the case that not all modalities could be available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to learn robust representations leveraging multimodal data in the training stage, while considering limitations at test time, such as noisy or missing modalities. This paper presents a…
Infrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower sensitivity to lighting conditions and appearance variability. While the action recognition task on videos collected from visible spectrum imaging has received much attention, action recognition in IR videos is significantly less explored. Our objective is to exploit imaging data in this modality for the action recognition task. In this work, we propose a novel two-stream 3D convolutional neural network (CNN) architecture by introducing the discriminative c…
Facebook（脸书）；Dartmouth College（达特茅斯学院）；Carnegie Mellon University（卡内基梅隆大学）；Argo AI
Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models have not been able to keep up with the ever-increasing depth and sophistication of these networks. In this work, we propose an alternative approach to learning video representations that require no semantically labeled videos and instead leverages the years of effort in collecting and labeling large and clean still-image datasets. We do so by using state-of-th…
Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenario,i.e. between image and video. Currently,state-of-the-art approaches mainly focus on the task-specific data,neglecting the extra information on the different but related tasks. In this paper,we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra informat…
UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions
State Key Laboratory for Novel Software Technology（新型软件技术国家重点实验室）；Tencent Hunyuan（腾讯文英）；Shanghai Jiao Tong University（上海交通大学）；Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Shanghai AI Lab（上海人工智能实验室）
Learning Natural Consistency Representation for Face Forgery Video Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；RealAI
Enhancing Audio-Visual Spiking Neural Networks through Semantic-Alignment and Cross-Modal Residual Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Center for Long-term Artificial Intelligence（长期人工智能中心）；Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences（中国科学院脑认知与脑智能技术重点实验室）
Narrating For You: Prompt-guided Audio-visual Narrating Face Generation Employing Multi-entangled Latent Space
Machine Intelligence Group, Department of CS&IS, BITS Pilani, Hyderabad Campus, India（机器智能组，计算机科学与信息学系，比斯汉学院海得拉巴校区，印度）；Georgia Institute of Technology, USA（佐治亚理工学院，美国）
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models
Institute of Image Communication and Information Processing, Shanghai Jiao Tong University（上海交通大学图像通信与信息处理研究所）；MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院（教育部人工智能重点实验室））；Huawei Technologies（华为技术有限公司）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Massachusetts Institute of Technology（麻省理工学院）
Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational m…
Stanford University（斯坦福大学）；Renmin University of China（中国人民大学）
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogeneous data within neighborhoods. In this work, we propose Urban2Vec, an unsupervised multi-modal framework which incorporates both street view imagery and point-of-interest (POI) data to learn neighborhood embeddings. Specifically, we use a convolutional neural network to extract …
MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
Graduate School of Informatics, Nagoya University（名古屋大学情报学研究科）；Institutes of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新机构）；Graduate School of Engineering, Gifu University（岐阜大学工学研究科）；Tier IV Inc.（Tier IV公司）
