15 papers · 1 filter
Memory-Supported Synergistic Adaptation for Training-Free Test-Time Medical Image Segmentation
Lingrui Li, Nan Pu, Dong Zhao +4
Test-time adaptation (TTA) aims to mitigate distribution shifts by adapting models with unlabeled target data at inference time. While TTA with vision-language models (VLMs) has sh…
Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding
Boyan Wang, Yongxi Huang, Wenjing Li +4
This report presents our solution for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge, which requires parsing unstructured outdoor scenes from four camera platf…
OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics
Jinjie Shen, Zheng Huang, Yuchen Zhang +7
Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the model alone. However, self-con…
OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL
Jinjie Shen, Jing Wu, Yaxiong Wang +5
Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and videos prevalent in real-world misinform…
Open-Vocabulary Domain Generalization in Urban-Scene Segmentation
Dong Zhao, Qi Zang, Nan Pu +3
Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restri…
Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation
Lingrui Li, Yanfeng Zhou, Nan Pu +2
Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation mod…