In this article, we introduce SiamMask, a framework to perform both visual object tracking
and video object segmentation, in real-time, with the same simple method. We improve the …

Unsupervised learning has been popular in various computer vision tasks, including visual
object tracking. However, prior unsupervised tracking approaches rely heavily on spatial …

In this paper, we propose to learn an Unsupervised Single Object Tracker (USOT) from
scratch. We identify that three major challenges, ie, moving object discovery, rich temporal …

Abstract We introduce Diff-Tracker, a novel approach for the challenging unsupervised
visual tracking task leveraging the pre-trained text-to-image diffusion model. Our main idea …

In this paper, we propose a progressive unsupervised learning (PUL) framework, which
entirely removes the need for annotated training videos in visual tracking. Specifically, we …

Millimeter wave radar is a promising sensing modality for enabling pervasive and privacy-
preserving human sensing. However, the lack of large-scale radar datasets limits the …

In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to
address the video-based crowd counting problem, which contains the decomposition of 3D …

3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in
autonomous driving. Current approaches all follow the Siamese paradigm based on …

Generalized zero-shot Learning (GZSL) aims to recognize images from either seen or
unseen domain, mainly by learning a joint embedding space to associate image features …

The discriminative model prediction (DiMP) object tracking model is an excellent end-to-end
tracking framework and have achieved the best results of its time. However, there are two …