3 accepted papers
Do we still need to represent objects explicitly in multimodal large language models (MLLMs)? To one extreme, pre-trained encoders convert images into visual tokens, with which objects and spatiotemporal relationships may be implicitly modeled. To the other extreme, image captions by themselves prov…
Recognizing the physical states of objects and their transformations within videos is crucial for structured video understanding and enabling robust real-world applications, such as robotic manipulation. However, pretrained vision-language models often struggle to capture these nuanced dynamics and…
Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary loss between the feature maps of intermediate layers and the ground truth in the field of semantic segmentation, the size of each feature map m…