Structural health monitoring at local and global levels using computer vision technologies
has gained much attention in the structural health monitoring community in research and …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

In this paper, we present a new sequence-to-sequence learning framework for visual
tracking, dubbed SeqTrack. It casts visual tracking as a sequence generation problem …

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking
tributaries. To inherit the powerful representations of the foundation model, a natural modus …

Tracking often uses a multi-stage pipeline of feature extraction, target information
integration, and bounding box estimation. To simplify this pipeline and unify the process of …

Optimization based tracking methods have been widely successful by integrating a target
model prediction module, providing effective global reasoning by minimizing an objective …

Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its
impressive segmentation performance on images. Regarding its strong ability on image …

We present a method that simultaneously addresses the tasks of dynamic scene novel-view
synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We …

In video object tracking, there exist rich temporal contexts among successive frames, which
have been largely overlooked in existing trackers. In this work, we bridge the individual …

Anchor-based Siamese trackers have achieved remarkable advancements in accuracy, yet
the further improvement is restricted by the lagged tracking robustness. We find the …