5 accepted papers
Large Language Models (LLMs) frequently exhibit strong translation abilities, even without task-specific fine-tuning. However, the internal mechanisms governing this innate capability remain largely opaque. To demystify this process, we leverage Sparse Autoencoders (SAEs) and introduce a novel frame
Crown preparation aims to create an optimal foundation for durable and functional restoration by reshaping the tooth with a cutting tool. Robotic crown preparation has emerged as a promising approach to overcome the inherent limitations of manual procedures, yet challenges remain in achieving effici…
Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its potential for bridging the gap between vision and language modalities. However, advancements in this domain are currently constrained by limitations inherent in existin…
Referring video object segmentation (RVOS), as a supervised learning task, relies on sufficient annotated data for a given scene. However, in more realistic scenarios, only minimal annotations are available for a new scene, which poses significant challenges to existing RVOS methods. With this in mi…
Many machine learning models lack the consideration that an adversary can alter data at the time of training or testing. Over the past decade, the machine learning models' vulnerability has been a concern and more secure algorithms are needed. Unfortunately, the security of feature selection (FS) re…