5 accepted papers
This paper proposes a real-time, generalizable trajectory prediction framework that integrates neural networks with physical modeling, applicable to various ball sports. Unlike existing methods that are tailored to specific ball types and lack scalability, our approach addresses two core challenges:
The synergistic framework of multimodal large language models (MLLMs) and vision foundation models demonstrates exceptional performance in image understanding tasks, yet encounters severe temporal inconsistency challenges in video segmentation scenarios. Existing methods predominantly rely on MLLMs
Medical image segmentation is pivotal in quantifying tissue volumes, facilitating diagnoses, and enabling other critical medical applications. However, accurately segmenting medical images can be challenging because the complex intensity distribution inherent in the data arises from the highly compl…
Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or op…
The task of point cloud segmentation, comprising semantic, instance, and panoptic segmentation, has been mainly tackled by designing task-specific network architectures, which often lack the flexibility to generalize across tasks, thus resulting in a fragmented research landscape. In this paper, we…