3 citations · 7 across the 18 of their papers we have counts for
18 papers
Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking
Hongtao Yang, Bineng Zhong, Qihua Liang +4
Given the real-time demands of UAV tracking, many methods simplify the backbone to reduce computation, but this often weakens feature representation and degrades performance in com…
An Efficient Token Compression Framework for Visual Object Tracking
Weijing Wu, Qihua Liang, Bineng Zhong +3
Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and computational cost of models in…
Learning to Track Instance from Single Nature Language Description
Yaozong Zheng, Bineng Zhong, Qihua Liang +3
How to achieve vision-language (VL) tracking using natural language descriptions from a video sequence \textbf{without relying on any bounding-box ground truth}? In this work, we a…
Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning
Yaozong Zheng, Qihua Liang, Bineng Zhong +4
Learning robust contextual knowledge from unlabeled videos is essential for advancing self-supervised tracking. However, conventional self-supervised trackers lack effective contex…
UBATrack: Spatio-Temporal State Space Model for General Multi-Modal Tracking
Qihua Liang, Liang Chen, Yaozong Zheng +3
Multi-modal object tracking has attracted considerable attention by integrating multiple complementary inputs (e.g., thermal, depth, and event data) to achieve outstanding performa…
Robust RGB-T Tracking via Learnable Visual Fourier Prompt Fine-tuning and Modality Fusion Prompt Generation
Hongtao Yang, Bineng Zhong, Qihua Liang +3
Recently, visual prompt tuning is introduced to RGB-Thermal (RGB-T) tracking as a parameter-efficient finetuning (PEFT) method. However, these PEFT-based RGB-T tracking methods typ…
Explicit Context Reasoning with Supervision for Visual Tracking
Fansheng Zeng, Bineng Zhong, Haiying Xia +4
Contextual reasoning with constraints is crucial for enhancing temporal consistency in cross-frame modeling for visual tracking. However, mainstream tracking algorithms typically a…
Towards Universal Modal Tracking with Online Dense Temporal Token Learning
Yaozong Zheng, Bineng Zhong, Qihua Liang +4
We propose a universal video-level modality-awareness tracking model with online dense temporal token learning (called {\modaltracker}). It is designed to support various tracking…
Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Tracking
Yaozong Zheng, Bineng Zhong, Qihua Liang +4
The success of visual tracking has been largely driven by datasets with manual box annotations. However, these box annotations require tremendous human effort, limiting the scale a…
Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking
Chaocan Xue, Bineng Zhong, Qihua Liang +4
Vision transformers (ViTs) have emerged as a popular backbone for visual tracking. However, complete ViT architectures are too cumbersome to deploy for unmanned aerial vehicle (UAV…
Dynamic Updates for Language Adaptation in Visual-Language Tracking
Xiaohai Li, Bineng Zhong, Qihua Liang +3
The consistency between the semantic information provided by the multi-modal reference and the tracked object is crucial for visual-language (VL) tracking. However, existing VL tra…
Adaptive Perception for Unified Visual Multi-modal Object Tracking
Xiantao Hu, Bineng Zhong, Qihua Liang +4
Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imba…
Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
Xiantao Hu, Ying Tai, Xu Zhao +5
Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing mult…
MambaLCT: Boosting Tracking via Long-term Context State Space Model
Xiaohai Li, Bineng Zhong, Qihua Liang +3
Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing wo…
Robust Tracking via Mamba-based Context-aware Token Learning
Jinxia Xie, Bineng Zhong, Qihua Liang +3
How to make a good trade-off between performance and computational cost is crucial for a tracker. However, current famous methods typically focus on complicated and time-consuming…
Autoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers
Jinxia Xie, Bineng Zhong, Zhiyi Mo +4
The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on…
Explicit Visual Prompts for Visual Object Tracking
Liangtao Shi, Bineng Zhong, Qihua Liang +3
How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus o…
ODTrack: Online Dense Temporal Token Learning for Visual Tracking
Yaozong Zheng, Bineng Zhong, Qihua Liang +3
Online contextual reasoning and association across consecutive video frames are critical to perceive instances in visual tracking. However, most current top-performing trackers per…