Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted significant attention due to the growing demand for video analysis. Recent approaches treat MR and HD as similar video grounding problems and address them together with transformer-based architecture. However, we observe that the emphasis of MR and HD differs, with one necessitating the perception of local relationships and the other prioritizing the understanding of global contexts. Consequently, the lack of task-specific design will inevitably lead to limitations in associating the intrinsic specialty of two tasks. To tackle the issue, we propose a Unified Video COMprehension framework (UVCOM) to bridge the gap and jointly solve MR and HD effectively. By performing progressive integration on intra and inter-modality across multi-granularity, UVCOM achieves the comprehensive understanding in processing a video. Moreover, we present multi-aspect contrastive learning to consolidate the local relation modeling and global knowledge accumulation via well aligned multi-modal space. Extensive experiments on QVHighlights, Charades-STA, TACoS , YouTube Highlights and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which outperforms the state-of-the-art methods by a remarkable margin.
Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap. Recent successes in deep learning enabled detection of objects belonging to many classes greatly outperforming traditional computer vision techniques. However, deep learning solutions capable of executing retrieval queries are still not available. We propose a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution. Specifically, we use YOLOv2 and OpenCog.…
Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages: e.g., mask language modeling objective requires local understanding of the masked token and the span-extraction objective requires global understanding and reasoning of the input …
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Kuaishou Technology（快手科技）；Huazhong University of Science and Technology（华中科技大学）
Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Nanjing University（南京大学）；University of Rochester（罗切斯特大学）
Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
Institute of Deep Perception Technology, JITRI（JITRI深度感知技术研究所）；University of Liverpool（利物浦大学）；University of Southampton（南安普顿大学）；Momoni AI（Momoni人工智能公司）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Thrust of Artificial Intelligence, HKUST (GZ)（香港科技大学（广州）人工智能学域）；Thrust of Intelligent Transportation, HKUST (GZ)（香港科技大学（广州）智能交通学域）
Faculty of Information Technology, Applied Science Private University（应用科学私立大学信息技术学院）
This paper addresses the problem of semantic-based image retrieval of natural scenes. A typical content-based image retrieval system deals with the query image and images in the dataset as a collection of low-level features and retrieves a ranked list of images based on the similarities between features of the query image and features of images in the image dataset. However, top ranked images in the retrieved list, which have high similarities to the query image, may be different from the query image in terms of the semantic interpretation of the user which is known as the semantic gap. In ord…
Remote Sensing Retrieval-Augmented Generation: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model
School of Cyber Science and Technology, University of Science and Technology of China（信息科学技术学院，中国科学技术大学）；China Academy of Electronics and Information Technology（电子信息技术研究院）
Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders
King Abdullah University of Science and Technology（国王阿卜杜勒-阿齐兹大学科学与技术大学）；Visual Recognition Group（视觉识别组）；Faculty of Electrical Engineering（电气工程学院）；Czech Technical University in Prague（布拉格捷克技术大学）
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
Goethe University Frankfurt（法兰克福大学）；Max-Planck-Institute for Informatics（马克斯·普朗克信息学研究所）；University of Bonn（波恩大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
The University of Hong Kong（香港大学）；ARC Lab（ARC实验室）；UC Berkeley（加州大学伯克利分校）；Content Understanding Center（内容理解中心）；Tencent PCG（腾讯平台与内容事业群）
Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Q…
Bridging the Modality Gap in Forensic Image Retrieval
Advanced Technologies Application Center (CENATAV)（先进技术应用中心（CENATAV））；Centro de Sistemas Complejos, Facultad de Física, Universidad de La Habana（哈瓦那大学物理学院复杂系统中心）
Roux Institute（鲁克斯研究所）；Northeastern University（东北大学）；University of Maine（缅因大学）
We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related tas…
RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning
Department of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；Department of Computer Science, City University of Hong Kong（城市大学计算机科学系）；Department of Computer Science, McGill University（麦吉尔大学计算机科学系）；School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学玛丽女王学院电子工程与计算机科学学院）；Department of Computer Science, Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学计算机科学系）
School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University（西北工业大学计算机学院暨光学影像分析与学习中心）
Image is usually taken for expressing some kinds of emotions or purposes, such as love, celebrating Christmas. There is another better way that combines the image and relevant song to amplify the expression, which has drawn much attention in the social network recently. Hence, the automatic selection of songs should be expected. In this paper, we propose to retrieve semantic relevant songs just by an image query, which is named as the image2song problem. Motivated by the requirements of establishing correlation in semantic/content, we build a semantic-based song retrieval framework, which lear…
Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description. Previous methods tend to perform self-modal learning and cross-modal interaction in a coarse manner, which neglect fine-grained clues contained in video content, query context, and their alignment. To this end, we propose a novel Multi-Granularity Perception Network (MGPN) that perceives intra-modality and inter-modality information at a multi-granularity level. Specifically, we formulate moment retrieval as a multi-choice reading comprehensi…
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval
School of Computer Science and Technology, Xi’an University of Posts and Telecommunications（西安邮电大学计算机科学与技术学院）；Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing（陕西省网络数据分析与智能处理重点实验室）；Xi’an Key Laboratory of Big Data and Intelligent Computing（西安大数据与智能计算重点实验室）；School of Computer Engineering, Weifang University（潍坊大学计算机工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Electronic and Electrical Engineering, Brunel University of London（伦敦布鲁内尔大学电子与电气工程系）；Dept. Matemàtiques i Informàtica and Institute of Neuroscience, Univeritat de Barcelona（数学与计算机系和神经科学研究所，巴塞罗那大学）
Zhejiang University（浙江大学）；Singapore Management University（新加坡管理大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community. One of the most popular research topics in this domain is Food Retrieval, due to its profound influence on health-oriented applications. In this paper, we focus on the task of cross-modal retrieval between food images and cooking recipes. We present Modality-Consistent Embedding Network (MCEN) that learns modality-invariant representations by projecting images and texts to the same embedding space. To capture the latent alignments between mod…
BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University（中国教育部智能感知与图像理解重点实验室，西安电子科技大学）；School of Telecommunications, Xidian University（西安电子科技大学电信学院）；Department of Automation, Tsinghua University（清华大学自动化系）
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. The semantic gap problem is reviewed and a survey on recent efforts in bridging the gap is made in this work. Most importantly, we claim that the semantic gap is primarily bridged through supervised learning today. Experiences are drawn from two application domains to illustrate this point: 1) object detection and 2) metric learning for content-based image retrieval (CBIR). To begin with, this pape…
Guangzhou University（广州大学）；University of Macau（澳门大学）
Visible-infrared cross-modality person re-identification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Most existing works integrate batch normalization layers into their neural network, but we found out that batch normalization layers would lead to two types of distribution gap: 1) inter-mini-batch distribution gap -- the distribution gap of the same modality between each mini-batch; 2) intra-mini-batch modality distribution gap -- the distribution gap of different modality within the same mini-batch…
