GateMOT: Q-Gated Attention for Dense Object Tracking
Huazhong University of Science and Technology, Wuhan, China（华中科技大学，武汉，中国）；Zhejiang University, Hangzhou, China（浙江大学，杭州，中国）；La Trobe University, Melbourne, Australia（拉筹伯大学，墨尔本，澳大利亚）
While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tracking: its quadratic all-to-all interactions are computationally prohibitive for dense motion estimation on high-resolution features. This mismatch prevents Dense Object Tracking from fully leveraging attention-based modeling in crowded and occlusion-heavy scenes. To address this challenge, we introduce GateMOT, an online tracking framework centered on Q-Gated Attention (Q-Attention), an efficient and spatially aware attention variant. Our key idea is to repurpose the Query from a similarity-conditioning term into a learnable gating unit. This Gating-Query (Gating-Q) produces a probabilistic gate that modulates Key features in an element-wise manner, enabling explicit relevance selection instead of costly global aggregation. Built on this mechanism, parallel Q-Attention heads transform one shared feature map into task-specific yet consistent representations for detection, motion, and re-identification, yielding a tightly coupled multi-task decoder with linear-complexity gating operations. GateMOT achieves state-of-the-art HOTA of 48.4, MOTA of 67.8, and IDF1 of 64.5 on BEE24, and demonstrates strong performance on additional Dense Object Tracking benchmarks. These results show that Q-Attention is a simple, effective, and transferable building block for attention-based tracking in dense tracking scenarios.
Fuzhou University（福州大学）；Tencent AI Lab（腾讯人工智能实验室）；Jiangnan University（江南大学）；South China University of Technology（华南理工大学）；City University of Hong Kong（香港城市大学）
The tracking-by-detection framework receives growing attentions through the integration with the Convolutional Neural Networks (CNNs). Existing tracking-by-detection based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection fra…
Efficient Language-to-Vision Feature Injection for Referring Single-Object Tracking
Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间应用工程与技术中心）；University of Chinese Academy of Sciences（中国科学院大学）
Multi-Object Tracking (MOT) is a challenging task in the complex scene such as surveillance and autonomous driving. In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU). The tracklet generation utilizes object features extracted by CNN and RNN to create the high-confidence tracklet candidates in sparse scenario. Due to mis-tracking in the generation process, the tracklets from different objects are split into several sub-tracklets by a bidirectional GRU. After that, a Siamese GRU…
Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Low-quality modalities contain not only a lot of noisy information but also some discriminative features in RGBT tracking. However, the potentials of low-quality modalities are not well explored in existing RGBT tracking algorithms. In this work, we propose a novel duality-gated mutual condition network to fully exploit the discriminative information of all modalities while suppressing the effects of data noise. In specific, we design a mutual condition module, which takes the discriminative information of a modality as the condition to guide feature learning of target appearance in another mo…
Template-matching methods for visual tracking have gained popularity recently due to their comparable performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. An LSTM is used as a memory controller, where the input is the search feature map and the outputs are the control signals for the reading and writing process of the memory block. As the loc…
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（杭州高等研究院，中国科学院大学）；Dept. of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳州立大学）；Institute of Software Chinese Academy of Science（中国科学院软件研究所）
Template-matching methods for visual tracking have gained popularity recently due to their good performance and fast speed. However, they lack effective ways to adapt to changes in the target object's appearance, making their tracking accuracy still far from state-of-the-art. In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking. The reading and writing process of the external memory is controlled by an LSTM network with the search feature map as input. A spatial attention mechanism is applied to concentrate the LSTM inpu…
Shanghai Grandhonor Information Technology Co.Ltd（上海宏景信息科技有限公司）；Nanjing University of Aeronautics and Astronautic（南京航空航天大学）；New York University（纽约大学）
Multiple Object Tracking (MOT) has been a useful yet challenging task in many real-world applications such as video surveillance, intelligent retail, and smart city. The challenge is how to model long-term temporal dependencies in an efficient manner. Some recent works employ Recurrent Neural Networks (RNN) to obtain good performance, which, however, requires a large amount of training data. In this paper, we proposed a novel tracking method that integrates the auto-tuning Kalman method for prediction and the Gated Recurrent Unit (GRU) and achieves a near-optimum with a small amount of trainin…
SwiTrack: Tri-State Switch for Cross-Modal Object Tracking
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Mathematics, Southeast University（东南大学数学学院）；Purple Mountain Laboratories（紫金山实验室）
We propose a recurrent neural network-based spatio-temporal framework named maskGRU for the detection and tracking of small objects in videos. While there have been many developments in the area of object tracking in recent years, tracking a small moving object amid other moving objects and actors (such as a ball amid moving players in sports footage) continues to be a difficult task. Existing spatio-temporal networks, such as convolutional Gated Recurrent Units (convGRUs), are difficult to train and have trouble accurately tracking small objects under such conditions. To overcome these diffic…
Geometric-Topological Perception and Motion Prior for Real-Time Satellite Video Object Tracking
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统技术重点实验室）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子、电气与通信工程学院）
Pedestrian tracking has long been considered an important problem, especially in security applications. Previously,many approaches have been proposed with various types of sensors. One popular method is Pedestrian Dead Reckoning(PDR) [1] which is based on the inertial measurement unit(IMU) sensor. However PDR is an integration and threshold based method, which suffers from accumulation errors and low accuracy. In this paper, we propose a novel method in which the sensor data is fed into a deep learning model to predict the displacements and orientations of the pedestrian. We also devise a new …
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Robotics Research Center（机器人研究中心）
We present a monocular object parsing framework for consistent keypoint localization by capturing temporal correlation on sequential data. In this paper, we propose a novel recurrent network based architecture to model long-range dependencies between intermediate features which are highly useful in tasks like keypoint localization and tracking. We leverage the expressiveness of the popular stacked hourglass architecture and augment it by adopting memory units between intermediate layers of the network with weights shared across stages for video frames. We observe that this weight sharing schem…
GRC-Pose: Generation-Reconstruction Correspondence for Prior-Free 6D Object Pose Tracking
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；Zhongguancun Academy（中关村学院）；Artificial Intelligence Academy, Xidian University（西安电子科技大学人工智能学院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）
Towards Universal Modal Tracking with Online Dense Temporal Token Learning
Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education（教育区块链与智能技术重点实验室，教育部）；Guangxi Key Laboratory of Multi-Source Information Mining and Security, Guangxi Normal University（广西多源信息挖掘与安全重点实验室，广西师范大学）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；University of Chinese Academy of Sciences（中国科学院大学）；Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University（媒体分析与计算实验室，人工智能系，厦门大学）
University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
Face alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking and animation to higher-level classification problems such as face, expression or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability cau…
Fudan University（复旦大学）；Meituan（美团）；Shanghai Collaborative Innovation Center on Intelligent Visual Computing（上海智能视觉计算协同创新中心）
Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. This is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques. However, depth information is under-investigated in these approaches when …
TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation
University of Arkansas, USA（阿肯色大学）；Max Planck Research School for Intelligent Systems（马克斯·普朗克智能系统研究所）；Google Research, Google（谷歌研究院）；University of Liverpool, UK（利物浦大学）；Adobe Research（Adobe研究院）
Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating
