Visual target tracking is one of the most sought-after yet challenging research topics in
computer vision. Given the ill-posed nature of the problem and its popularity in a broad …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

We present a unified method, termed Unicorn, that can simultaneously solve four tracking
problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters …

The presence of objects that are confusingly similar to the tracked target, poses a
fundamental challenge in appearance-based visual tracking. Such distractor objects are …

Abstract We present Siam R-CNN, a Siamese re-detection architecture which unleashes the
full power of two-stage object detection approaches for visual object tracking. We combine …

In this paper we illustrate how to perform both visual object tracking and semi-supervised
video object segmentation, in real-time, with a single simple approach. Our method, dubbed …

This paper proposes a simple self-supervised approach for learning a representation for
visual correspondence from raw video. We cast correspondence as prediction of links in a …

We introduce here a large tracking database that offers an unprecedentedly wide coverage
of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon …

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object
Tracking. LaSOT consists of 1,400 sequences with more than 3.5 M frames in total. Each …

Tracking pixels in videos is typically studied as an optical flow estimation problem, where
every pixel is described with a displacement vector that locates it in the next frame. Even …