5 papers · 1 filter
An Instance-Aware Prompting Framework for Training-free Camouflaged Object Segmentation
Chao Yin, Jide Li, Hang Yao +1
Training-free Camouflaged Object Segmentation (COS) seeks to segment camouflaged objects without task-specific training, by automatically generating visual prompts to guide the Seg…
See Different, Think Better: Visual Variations Mitigating Hallucinations in LVLMs
Ziyun Dai, Xiaoqiang Li, Shaohua Zhang +2
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in visual understanding and multimodal reasoning. However, LVLMs frequently exhibit hallucination phe…
Stepwise Decomposition and Dual-stream Focus: A Novel Approach for Training-free Camouflaged Object Segmentation
Chao Yin, Hao Li, Kequan Yang +3
While promptable segmentation (\textit{e.g.}, SAM) has shown promise for various segmentation tasks, it still requires manual visual prompts for each object to be segmented. In con…
DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation
Yuanchen Wu, Xichen Ye, Kequan Yang +2
Recently, One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage coun…
Unsupervised Learning of Multi-level Structures for Anomaly Detection
Songmin Dai, Jide Li, Lu Wang +3
The main difficulty in high-dimensional anomaly detection tasks is the lack of anomalous data for training. And simply collecting anomalous data from the real world, common distrib…