Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
As permutations of instance IDs are also valid solutions, the task requires learning of high …

We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

Most online multi-object trackers perform object detection stand-alone in a neural net without
any input from tracking. In this paper, we present a new online joint detection and tracking …

We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-
stage end-to-end system that treats instances of interest as learnable queries, enabling …

In recent years, video instance segmentation (VIS) has been largely advanced by offline
models, while online models gradually attracted less attention possibly due to their inferior …

Open-world object detection (OWOD) is a challenging computer vision problem, where the
task is to detect a known set of object categories while simultaneously identifying unknown …

Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in
which instance masks are derived by querying the image feature using a set of instance …

In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO
predicts masks via dynamic convolutions between panoptic kernels and image feature …

Video objection detection is a challenging task because isolated video frames may
encounter appearance deterioration, which introduces great confusion for detection. One of …

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-
performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch …