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Found 5 papers, 4 papers with code
I$^2$MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation
Different from existing distillation solutions that transfer the knowledge of a pre-trained and fixed teacher to the student, in CMD, the knowledge is continuously updated and bidirectionally distilled between modalities during pre-training.
Masked Motion Predictors are Strong 3D Action Representation Learners
To be specific, the proposed MAMP takes as input the masked spatio-temporal skeleton sequence and predicts the corresponding temporal motion of the masked human joints.
Ranked #5 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Detect Any Shadow: Segment Anything for Video Shadow Detection
Segment anything model (SAM) has achieved great success in the field of natural image segmentation.
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
In this work, we formulate the cross-modal interaction as a bidirectional knowledge distillation problem.
Joint Inductive and Transductive Learning for Video Object Segmentation
In this work, we propose to integrate transductive and inductive learning into a unified framework to exploit the complementarity between them for accurate and robust video object segmentation.
