173 citations · 374 across the 20 of their papers we have counts for
7 papers · 1 filter
Stable Segment Anything Model
Qi Fan, Xin Tao, Lei Ke +6
The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts which, however, often require good skills to specify. To make SAM robust to…
Gaussian Grouping: Segment and Edit Anything in 3D Scenes
Mingqiao Ye, Martin Danelljan, Fisher Yu +1
The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of the 3D scenes. However, it is solely concentrated on the appearance and geometry modeling,…
Cascade-DETR: Delving into High-Quality Universal Object Detection
Mingqiao Ye, Lei Ke, Siyuan Li +4
Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not comp…
Segment Anything Meets Point Tracking
Frano Rajič, Lei Ke, Yu-Wing Tai +3
The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. Whi…
Segment Anything in High Quality
Lei Ke, Mingqiao Ye, Martin Danelljan +4
The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being…
OVTrack: Open-Vocabulary Multiple Object Tracking
Siyuan Li, Tobias Fischer, Lei Ke +3
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional m…
Mask-Free Video Instance Segmentation
Lei Ke, Martin Danelljan, Henghui Ding +3
The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks…