4 accepted papers
Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for system…
The increasing demands in analyzing complex associated scenes pose necessities to researching multi-image understanding abilities. Compared with understanding individual images, both the alignments and differences between images are essential aspects of understanding the intricate relationships for
The complexities of table structures and question logic make table-based question answering (TQA) tasks challenging for Large Language Models (LLMs), often requiring task simplification before solving. This paper reveals that the reasoning process during task simplification may be more valuable than…
We propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorder (AD). By leveraging a learnable incidence matrix, seHGNN strengthens the influence of hyperedges in graphs and enhances feature extraction performance of HGNNs. Then, we conduct this model within an ex…