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 …

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 …

Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which
allows earlier interaction between the template and search region, has achieved a …

Transformer-based trackers have achieved strong accuracy on the standard benchmarks.
However, their efficiency remains an obstacle to practical deployment on both GPU and …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

Transformer-based visual trackers have demonstrated significant progress owing to their
superior modeling capabilities. However, existing trackers are hampered by low speed …

Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

Tracking by natural language specification aims to locate the referred target in a sequence
based on the natural language description. Existing algorithms solve this issue in two steps …

Visual tracking is a fundamental task in computer vision with significant practical
applications in various domains, including surveillance, security, robotics, and human …

Transformer framework has been showing superior performances in visual object tracking
for its great strength in information aggregation across the template and search image with …