3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes
Qifan Zhang, Sai Haneesh Allu, Jikai Wang +2
Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of template images, a robot must l…
Multimodal Reference Visual Grounding
Yangxiao Lu, Ruosen Li, Liqiang Jing +5
Visual grounding focuses on detecting objects from images based on language expressions. Recent Large Vision-Language Models (LVLMs) have significantly advanced visual grounding pe…
Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation
Yangxiao Lu, Jishnu Jaykumar P, Yunhui Guo +2
Novel Instance Detection and Segmentation (NIDS) aims at detecting and segmenting novel object instances given a few examples of each instance. We propose a unified, simple, yet ef…
RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant Features
Howard H. Qian, Yangxiao Lu, Kejia Ren +4
In order to successfully perform manipulation tasks in new environments, such as grasping, robots must be proficient in segmenting unseen objects from the background and/or other o…
Mean Shift Mask Transformer for Unseen Object Instance Segmentation
Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi +1
Segmenting unseen objects from images is a critical perception skill that a robot needs to acquire. In robot manipulation, it can facilitate a robot to grasp and manipulate unseen…