5 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…
SPWOOD: Sparse Partial Weakly-Supervised Oriented Object Detection
Wei Zhang, Xiang Liu, Ningjing Liu +4
A consistent trend throughout the research of oriented object detection has been the pursuit of maintaining comparable performance with fewer and weaker annotations. This is partic…
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…
Semantic Discrepancy-aware Detector for Image Forgery Identification
Ziye Wang, Minghang Yu, Chunyan Xu +1
With the rapid advancement of image generation techniques, robust forgery detection has become increasingly imperative to ensure the trustworthiness of digital media. Recent resear…
LLM-Assisted Semantic Guidance for Sparsely Annotated Remote Sensing Object Detection
Wei Liao, Chunyan Xu, Chenxu Wang +1
Sparse annotation in remote sensing object detection poses significant challenges due to dense object distributions and category imbalances. Although existing Dense Pseudo-Label me…