Search Results for author:
Found 6 papers, 4 papers with code
Learning Macroeconomic Policies based on Microfoundations: A Stackelberg Mean Field Game Approach
Effective macroeconomic policies play a crucial role in promoting economic growth and social stability.
Asymmetric Masked Distillation for Pre-Training Small Foundation Models
And AMD achieves 73. 3% classification accuracy using the ViT-B model on the Something-in-Something V2 dataset, a 3. 7% improvement over the original ViT-B model from VideoMAE.
Ranked #20 on
Action Recognition
on Something-Something V2
MGMAE: Motion Guided Masking for Video Masked Autoencoding
Based on this masking volume, we can track the unmasked tokens in time and sample a set of temporal consistent cubes from videos.
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90. 0% on K400 and 89. 9% on K600) and Something-Something (68. 7% on V1 and 77. 0% on V2).
Ranked #1 on
Self-Supervised Action Recognition
on UCF101
(using extra training data)
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications.
Ranked #1 on
Action Recognition
on Something-Something V1
(using extra training data)
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
In this report, we present our champion solutions to five tracks at Ego4D challenge.
Ranked #1 on
State Change Object Detection
on Ego4D
