6 accepted papers
Large multimodal models (LMMs) have shown promising performance for various document recognition tasks. However, LMMs adopt implicit modeling, and the parameters lack interpretability. Inspired by recent advances in human memory and learning research, we propose an explicit multiscale prototype memo
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks, yet often suffer from inefficiencies due to redundant visual tokens. Existing token merging methods reduce sequence length but frequently disrupt spatial layouts and temporal continuity by disregardin…
Few-shot counting estimates the number of target objects in an image using only a few annotated exemplars. However, domain shift severely hinders existing methods to generalize to unseen scenarios. This falls into the realm of single domain generalization that remains unexplored in few-shot countin…
Video action segmentation refers to the task of densely casting each video frame or short segment in an untrimmed video into some pre-specified action categories. Although recent years have witnessed a great promise in the development of action segmentation techniques.A large body of existing method…
Vanilla convolutions in modern deep networks are known to operate locally and at fixed scale (e.g., the widely-adopted 3*3 kernels in image-oriented tasks). This causes low efficacy in connecting two distant locations in the network. In this work, we propose a novel convolutional operator dubbed as…
Modern CNN-based object detectors rely on bounding box regression and non-maximum suppression to localize objects. While the probabilities for class labels naturally reflect classification confidence, localization confidence is absent. This makes properly localized bounding boxes degenerate during i…