4 citations · 13 across the 20 of their papers we have counts for
21 papers
VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance
Yunfeng Liu, Yuandong Yang, Jiarui Han +5
Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achi…
VL-UniTrack: A Unified Framework with Visual-Language Prompts for UAV-Ground Visual Tracking
Boyue Xu, Ruichao Hou, Tongwei Ren +1
UAV-ground visual tracking (UGVT) aims to simultaneously track the same object from both the UAV and the ground view. However, existing two-stream methods suffer from isolated feat…
VMonarch: Efficient Video Diffusion Transformers with Structured Attention
Cheng Liang, Haoxian Chen, Liang Hou +4
The quadratic complexity of the attention mechanism severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the highly sparse spatio-temporal a…
SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation
Jiaming Zhang, Shengming Cao, Rui Li +8
Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Binding process of the dominant Refer…
SAM 2++: Tracking Anything at Any Granularity
Jiaming Zhang, Cheng Liang, Yichun Yang +7
Due to the varying granularity of target states across different tasks, most existing trackers are tailored to a single task, which specificity limits their generalization, prevent…
MotionRAG: Motion Retrieval-Augmented Image-to-Video Generation
Chenhui Zhu, Yilu Wu, Shuai Wang +2
Image-to-video generation has made remarkable progress with the advancements in diffusion models, yet generating videos with realistic motion remains highly challenging. This diffi…
CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning
Jiange Yang, Yansong Shi, Haoyi Zhu +6
Unsupervised learning of latent motion from Internet videos is crucial for robot learning. Existing discrete methods generally mitigate the shortcut learning caused by extracting e…
RGB-D Tracking via Hierarchical Modality Aggregation and Distribution Network
Boyue Xu, Yi Xu, Ruichao Hou +3
The integration of dual-modal features has been pivotal in advancing RGB-Depth (RGB-D) tracking. However, current trackers are less efficient and focus solely on single-level featu…
RGB-D Video Object Segmentation via Enhanced Multi-store Feature Memory
Boyue Xu, Ruichao Hou, Tongwei Ren +1
The RGB-Depth (RGB-D) Video Object Segmentation (VOS) aims to integrate the fine-grained texture information of RGB with the spatial geometric clues of depth modality, boosting the…
KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection
Xingyuan Li, Ruichao Hou, Tongwei Ren +1
Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to identify visually significant objects by leveraging both RGB and thermal modalities to enable robust perfor…
Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning
Jiange Yang, Haoyi Zhu, Yating Wang +3
Learning from multiple domains is a primary factor that influences the generalization of a single unified robot system. In this paper, we aim to learn the trajectory prediction mod…
Efficient Test-Time Prompt Tuning for Vision-Language Models
Yuhan Zhu, Guozhen Zhang, Chen Xu +4
Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of te…
AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
Yuhan Zhu, Yuyang Ji, Zhiyu Zhao +2
Pre-trained vision-language models (VLMs) have shown impressive results in various visual classification tasks. However, we often fail to fully unleash their potential when adaptin…
Open-Vocabulary Spatio-Temporal Action Detection
Tao Wu, Shuqiu Ge, Jie Qin +2
Spatio-temporal action detection (STAD) is an important fine-grained video understanding task. Current methods require box and label supervision for all action classes in advance.…
STMixer: A One-Stage Sparse Action Detector
Tao Wu, Mengqi Cao, Ziteng Gao +2
Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract…
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos
Tao Wu, Runyu He, Gangshan Wu +1
Video-based visual relation detection tasks, such as video scene graph generation, play important roles in fine-grained video understanding. However, current video visual relation…
Dual DETRs for Multi-Label Temporal Action Detection
Yuhan Zhu, Guozhen Zhang, Jing Tan +2
Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos. Inspired by the success of DETR in object detection,…
Spatiotemporal Predictive Pre-training for Robotic Motor Control
Jiange Yang, Bei Liu, Jianlong Fu +3
Robotic motor control necessitates the ability to predict the dynamics of environments and interaction objects. However, advanced self-supervised pre-trained visual representations…
Asymmetric Masked Distillation for Pre-Training Small Foundation Models
Zhiyu Zhao, Bingkun Huang, Sen Xing +3
Self-supervised foundation models have shown great potential in computer vision thanks to the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing th…
Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation
Jiaming Zhang, Yutao Cui, Gangshan Wu +1
Current prevailing Video Object Segmentation methods follow the pipeline of extraction-then-matching, which first extracts features on current and reference frames independently, a…
Transferring Foundation Models for Generalizable Robotic Manipulation
Jiange Yang, Wenhui Tan, Chuhao Jin +6
Improving the generalization capabilities of general-purpose robotic manipulation agents in the real world has long been a significant challenge. Existing approaches often rely on…