6 accepted papers
Multimodal learning presents significant challenges arising from the unpredictable absence of modalities during both training and testing phases. Existing recovery methods struggle to leverage the available data, which can introduce additional noise during the recovery process and degrade performanc…
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Despite recent progress, current document parsing methods have not been fairly and comprehensively evaluated due to the nar…
Visual Question Answering (VQA) has garnered significant attention as a crucial link between vision and language, aimed at generating accurate responses to visual queries. However, current VQA models still struggle with the challenges of minority class collapse and spurious semantic correlations pos…
In this study, a bio-inspired crawling robot with multi-surface locomotion capability is developed. The robot is driven by Dielectric Elastomer Minimum Energy Structures (DEMES) and utilizes a three-dimensional scissor mechanism and electrostatic adhesion technology to achieve multi-surface crawling
Negative-pressure artificial muscles based on internal support and flexible skin provide a way to develop high-performance artificial muscles. However, the random and disordered skin wrinkles formed during the contraction may lead to uncertainty in the actuation behavior, and the flexible but inexte
Medical vision-language representation learning has garnered considerable attention owing to its applicability to extracting generic representations from the image and text modality. However, it still remains challenging to acquire a more comprehensive understanding of intra- and inter-modal semanti…