11 accepted papers
The application of large language models (LLMs) in healthcare holds significant promise for enhancing clinical decision-making, medical research, and patient care. However, their integration into real-world clinical settings raises critical concerns around trustworthiness, particularly around dimens
Mixture-of-Experts (MoE) models have become increasingly powerful in multimodal learning by enabling modular specialization across modalities. However, their effectiveness remains unclear when additional modalities introduce more noise than complementary information. Existing approaches, such as the
Robotic pose servoing aims to move the robot end-effector to the target pose. Closed-loop servo systems can tolerate a small TCF (tool control frame) calibration error and accurately reach the target pose through multiple pose measurements and pose adjustments. However, the maximum allowable TCF cal
Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG…
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identica…
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains). However most existing FL methods assume that domain…
Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the capacity gap between students and teachers becomes larger, existing KD methods fail to achieve better results. Our work…
This letter presents a novel robotic end-effector for screwing and unscrewing bolts. In many industrial scenarios, it is required to manipulate bolts from the side using robots with end-effectors. Besides, the reaction torque during tightening needs to be balanced to the stability of the robot. To a
Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of…
Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group representations. However, previous methods either model spatial and temporal information separately, or directly aggrega…
Few-shot learning aims to recognize unseen images of new classes with only a few training examples. While great progress has been made with deep learning technology, most metric-based works rely on the measurement based on global feature representation of images, which is sensitive to background fac…