41 citations · 44 across the 7 of their papers we have counts for
7 papers
Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
Runlong Cao, Ying Zang, Chuanwei Zhou +4
Semi-supervised referring expression segmentation (SS-RES) aims to achieve precise pixel-level language grounding under limited annotation, yet suffers from limited supervision and…
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
Tianrun Chen, Runlong Cao, Xinda Yu +8
The rapid rise of large-scale foundation models has reshaped the landscape of image segmentation, with models such as Segment Anything achieving unprecedented versatility across di…
Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches
Ying Zang, Yuncan Gao, Jiangi Zhang +7
This work advances zero-shot interactive segmentation for remote sensing imagery through three key contributions. First, we propose a novel sketch-based prompting method, enabling…
Let Human Sketches Help: Empowering Challenging Image Segmentation Task with Freehand Sketches
Ying Zang, Runlong Cao, Jianqi Zhang +8
Sketches, with their expressive potential, allow humans to convey the essence of an object through even a rough contour. For the first time, we harness this expressive potential to…
Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometry
Tianrun Chen, Chunan Yu, Yuanqi Hu +8
In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D…
RESMatch: Referring Expression Segmentation in a Semi-Supervised Manner
Ying Zang, Chenglong Fu, Runlong Cao +5
Referring expression segmentation (RES), a task that involves localizing specific instance-level objects based on free-form linguistic descriptions, has emerged as a crucial fronti…
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
Tianrun Chen, Lanyun Zhu, Chaotao Ding +6
The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for…