Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

Single-object tracking is regarded as a challenging task in computer vision, especially in
complex spatio-temporal contexts. The changes in the environment and object deformation …

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 …

Object tracking has achieved significant progress over the past few years. However, state-of-
the-art trackers become increasingly heavy and expensive, which limits their deployments in …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Most current RGB-T trackers adopt a two-stream structure to extract unimodal RGB and
thermal features and complex fusion strategies to achieve multi-modal feature fusion, which …

Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking
(MOT) for its concise pipeline. Many current works first associate the detections to form track …

Different from visible cameras which record intensity images frame by frame, the biologically
inspired event camera produces a stream of asynchronous and sparse events with much …

Recently, Siamese-based trackers have achieved promising performance in visual tracking.
Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW …

Thermal infrared (TIR) target tracking is susceptible to occlusion and similarity interference,
which obviously affects the tracking results. To resolve this problem, we develop an Aligned …