17 citations · 25 across the 16 of their papers we have counts for
16 papers
Learning to Track from Privileged Target Appearances
Xin Chen, Jiao Xu, Dong Wang +2
Target templates define what a visual tracker searches for, yet the templates available at inference trade off localization certainty with appearance freshness: the initial ground-…
AnyTrack: Unifying Visual Object Tracking with Any Modalities
Hao Li, Yunzhi Zhuge, Wenning Hao +4
Visual object tracking aims to continuously locate specific targets within sequential frames, evolving from single-modal methods to multi-modal ones. However, existing multi-modal…
RELO: Reinforcement Learning to Localize for Visual Object Tracking
Xin Chen, Chuanyu Sun, Jiao Xu +4
Conventional visual object trackers localize targets using handcrafted spatial priors, often in the form of heatmaps. Such priors provide only surrogate supervision and are poorly…
Eva-Tracker: ESDF-update-free, Visibility-aware Planning with Target Reacquisition for Robust Aerial Tracking
Yue Lin, Yang Liu, Dong Wang +1
The Euclidean Signed Distance Field (ESDF) is widely used in visibility evaluation to prevent occlusions and collisions during tracking. However, frequent ESDF updates introduce co…
CADTrack: Learning Contextual Aggregation with Deformable Alignment for Robust RGBT Tracking
Hao Li, Yuhao Wang, Xiantao Hu +4
RGB-Thermal (RGBT) tracking aims to exploit visible and thermal infrared modalities for robust all-weather object tracking. However, existing RGBT trackers struggle to resolve moda…
Complementary and Contrastive Learning for Audio-Visual Segmentation
Sitong Gong, Yunzhi Zhuge, Lu Zhang +2
Audio-Visual Segmentation (AVS) aims to generate pixel-wise segmentation maps that correlate with the auditory signals of objects. This field has seen significant progress with num…
Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
Ben Kang, Xin Chen, Jie Zhao +3
Transformer-based visual trackers have demonstrated significant advancements due to their powerful modeling capabilities. However, their practicality is limited on resource-constra…
Efficient Motion Prompt Learning for Robust Visual Tracking
Jie Zhao, Xin Chen, Yongsheng Yuan +3
Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this p…
Two-stream Beats One-stream: Asymmetric Siamese Network for Efficient Visual Tracking
Jiawen Zhu, Huayi Tang, Xin Chen +3
Efficient tracking has garnered attention for its ability to operate on resource-constrained platforms for real-world deployment beyond desktop GPUs. Current efficient trackers mai…
SUTrack: Towards Simple and Unified Single Object Tracking
Xin Chen, Ben Kang, Wanting Geng +4
In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event,…
Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future
Long Chen, Yuzhi Huang, Junyu Dong +5
Underwater object detection (UOD), aiming to identify and localise the objects in underwater images or videos, presents significant challenges due to the optical distortion, water…
MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking
Simiao Lai, Chang Liu, Jiawen Zhu +4
Existing RGB-T tracking algorithms have made remarkable progress by leveraging the global interaction capability and extensive pre-trained models of the Transformer architecture. N…
EvSign: Sign Language Recognition and Translation with Streaming Events
Pengyu Zhang, Hao Yin, Zeren Wang +5
Sign language is one of the most effective communication tools for people with hearing difficulties. Most existing works focus on improving the performance of sign language tasks o…
Exploring Dynamic Transformer for Efficient Object Tracking
Jiawen Zhu, Xin Chen, Haiwen Diao +6
The speed-precision trade-off is a critical problem for visual object tracking which usually requires low latency and deployment on constrained resources. Existing solutions for ef…
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
Jiazuo Yu, Yunzhi Zhuge, Lu Zhang +4
Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the p…
Leveraging the Power of Data Augmentation for Transformer-based Tracking
Jie Zhao, Johan Edstedt, Michael Felsberg +2
Due to long-distance correlation and powerful pretrained models, transformer-based methods have initiated a breakthrough in visual object tracking performance. Previous works focus…