most citedAutoregressive Queries for Adaptive Tracking with Spatio-TemporalTransformers

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20251 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV20243 cited

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…

cs.CV2024

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…