4 accepted papers
The concept bottleneck model (CBM), as a technique improving interpretability via linking predictions to human-understandable concepts, makes high-risk and life-critical medical image classification credible. Typically, existing CBM methods associate the final layer of visual encoders with concepts
Large language models (LLMs) show potential in healthcare but often generate hallucinations, especially when handling unfamiliar information. In medication, a systematic benchmark to evaluate model capabilities is lacking, which is critical given the high-risk nature of medical information. This pap…
The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs focusing on image-text alignment. However existing methods primarily rely on cosine similarity for alignment which may not fully capture the complex relatio…
Perception and being active (having a certain level of motion freedom) are closely tied. Learning active perception and sensorimotor control in the physical world is cumbersome as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given ris…