In this work we address various segmentation tasks each traditionally tackled by distinct or
partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Few-Shot Instance Segmentation (FSIS) requires detecting and segmenting novel classes
with limited support examples. Existing methods based on Region Proposal Networks …

Detecting objects accurately from a large or open vocabulary necessitates the vision-
language alignment on region representations. However, learning such a region-text …

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light
of the widespread adoption of vision-based foundational models. Its primary objective is to …

In this paper, we highlight a problem of evaluation metrics adopted in the open-vocabulary
segmentation. That is, the evaluation process still heavily relies on closed-set metrics on …

Open-vocabulary detection aims to detect objects from novel categories beyond the base
categories on which the detector is trained. However, existing open-vocabulary detectors …

Understanding the real world through point cloud video is a crucial aspect of robotics and
autonomous driving systems. However, prevailing methods for 4D point cloud recognition …

Open-vocabulary detection (OVD) aims to detect objects beyond a predefined set of
categories. As a pioneering model incorporating the YOLO series into OVD, YOLO-World is …

Enabling models to recognize vast open-world categories has been a longstanding pursuit
in object detection. By leveraging the generalization capabilities of vision-language models …