3 accepted papers
Transformers have achieved significant success in medical image segmentation, owing to their capability to capture long-range dependencies. Previous studies have employed either pure Transformer or hybrid CNN-Transformer architectures in the encoder module to enhance their ability to extract more co…
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in vision-language understanding. Recently, with the integration of test-time scaling techniques, these models have also shown strong potential in visual reasoning. However, most existing reasoning approaches remain t
Visual grounding, a crucial vision-language task involving the understanding of the visual context based on the query expression, necessitates the model to capture the interactions between objects, as well as various spatial and attribute information. However, the annotation data of visual grounding…