9 accepted papers
Large language models (LLMs) are increasingly used as planners for cooperative embodied agents, but multi-agent settings amplify inconsistency under partial observability and make explicit communication costly or even unavailable. Many existing approaches rely on online message passing; when communi…
Test-time adaptation (TTA) has emerged as a promising solution to address real world domain shifts in medical image segmentation. Current approaches adapt by updating or regularizing a pre-trained source model. However, they face two major issues: (i) the source models on which they rely are prone t
Recent advances in vision-language models (VLMs) have shown remarkable performance in medical image classification tasks. However, applying VLMs to fetal cardiac ultrasound (FCU) remains challenging due to compound distribution shifts, including covariate shifts caused by cross-center heterogeneity
Single Domain Generalization (SDG) is critical in medical imaging applications. Recently, Vision Foundation Models (VFMs) have spearheaded a trend in AI development due to their robust generalizability and versatility. This work aims to fully explore the generalization capabilities of VFMs alongside…
Medical imaging faces significant challenges in single-domain generalization (SDG) due to the diversity of imaging devices and the variability among data collection centers. To address these challenges, we propose \textbf{TinyMIG}, a framework designed to transfer generalization capabilities from vi…
Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robo…
Semantic segmentation is of great value to autonomous driving and many robotic applications, while it highly depends on costly and time-consuming pixel-level annotation. To make full use of unlabeled data, this work proposes a deep tri-training framework (dubbed DTT) to utilize labeled along with un
Hand pose estimation constitutes prime attainment for human-machine interaction-based applications. Real-time operation is vital in such tasks. Thus, a reliable estimator should exhibit low computational complexity and high precision at the same time. Previous works have explored the regression tech…
Estimating a scene’s depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field. This paper proposes a novel, low complexity network architecture for fast and accurate human depth estimation and segmentation in indoor environments, aimin…