Deep Learning
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Notably, our VideoMAE with the vanilla ViT backbone can achieve 87.4% on Kinects-400, 75.4% on Something-Something V2, 91.3% on UCF101, and 62.6% on HMDB51, without using any
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
The main idea is to use a generator to synthesize data for training the student.
LION: Latent Point Diffusion Models for 3D Shape Generation
Similarly, a user may want to interpolate different shapes.
GhostNetV2: Enhance Cheap Operation with Long-Range Attention Y ehui T ang
Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed.