Deep learning has been overwhelmingly successful in computer vision (CV), natural
language processing, and video/speech recognition. In this paper, our focus is on CV. We …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

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 …

We present ARTrack, an autoregressive framework for visual object tracking. ARTrack
tackles tracking as a coordinate sequence interpretation task that estimates object …

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 …

Visual object tracking aims to localize the target object of each frame based on its initial
appearance in the first frame. Depending on the input modility tracking tasks can be divided …

Abstract Multimodal Visual Object Tracking (VOT) has recently gained significant attention
due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which …

Object modeling has become a core part of recent tracking frameworks. Current popular
tackers use Transformer attention to extract the template feature separately or interactively …

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

Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-
defined anchor boxes to accurately estimate the scale and aspect ratio of a target …