6 accepted papers
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources, which poses significant challenges for medical m…
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources, which poses significant challenges for medical m…
Most AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various tasks. However, the classic task setup rarely considers the challenging, yet common practical situations where the complete visual data may be inaccessible due to various reaso…
While exploiting body segmentations for supervision, existing 3D human pose and shape estimation methods are plagued by mismatches between clothed body segmentations and skinned SMPL model reprojections. Moreover, noisy pixels introduced by inaccurate segmentation annotations also prevent the model…
The automatic detection of abnormal events in surveillance videos with weak supervision has been formulated as a multiple instance learning task, which aims to localize the clips containing abnormal events temporally with the video-level labels. However, most existing methods rely on the features ex…
Action recognition has wide applications in assisted living, health monitoring, surveillance, and human-computer interaction. In traditional action recognition methods, RGB video-based ones are effective but computationally inefficient, while skeleton-based ones are computationally efficient but do…