3 papers
cs.CV2026
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
cs.CV2025
Calibrating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
Lechao Cheng, Zerun Liu, Jingxuan He +3
Weakly supervised semantic segmentation (WSSS) has recently attracted considerable attention because it requires fewer annotations than fully supervised approaches, making it espec…
cs.CV2025
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Bin-Bin Gao, Yue Zhou, Jiangtao Yan +7
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studie…