5 accepted papers
Unsupervised Concept Extraction aims to extract concepts from a single image, yet existing methods suffer from the inability to extract composable intrinsic concepts. To address this, this paper introduces a new task called Compositional and Interpretable Intrinsic Concept Extraction (CI-ICE). The C
Automated auscultation advances the detection of respiratory diseases, especially in areas with limited resources where traditional diagnostic methods are unavailable. On the other hand, the scarcity of auscultation datasets limits the automation performance, prompting the needs for data augmentatio
Recent studies have shown that Reversible Adversarial Examples (RAE) can mislead unauthorized deep neural networks while remaining usable for authorized users, effectively preventing image data leakage. Existing RAE methods rely on reversibly embedding perturbation information into the original adve
Reliable medical audio diagnosis demands models that are not only accurate but also honest about their uncertainty. However, fine-tuned models based on small, imbalanced datasets often become overconfident due to norm bias, whereby they rely on feature magnitude rather than semantic alignment. As a …
Monocular 3D lane detection is essential for a reliable autonomous driving system and has recently been rapidly developing. Existing popular methods mainly employ a predefined 3D anchor for lane detection based on front-viewed (FV) space, aiming to mitigate the effects of view transformations. Howev…