4 accepted papers
Point cloud completion and generation are important across many 3D tasks, where both fidelity and sampling efficiency matter. Prevailing high-fidelity approaches rely on long sampling schedules, which incur substantial inference latency. Few-step alternatives typically use rectification or distillat…
Existing skeleton spatial-temporal models tend to deteriorate the positional distinguishability of skeleton joints and lead to inaccurate spatial matching and poor interpretability. This paper proposes a novel contrastive diffusion graph convolutional network (CD-GCN) for few-shot action recognition…
This paper presents Action-SGFA, a novel action feature alignment approach to learn unified joint embeddings across four action modalities incorporating scene graph (SG) comprehension. A new training paradigm for Action-SGFA is also devised to improve pre-trained VL models using datasets with SG ann…
This study explores unsupervised open-vocabulary skeleton action recognition, aiming at addressing inaccurate spatial matching and poor interpretability of existing GCN models. We present Skeleton-DGCFA, an approach to make feature alignment (FA) of skeleton with image modalities based on a large pr…