Search Results for author:
Found 6 papers, 4 papers with code
Learning Correlation Structures for Vision Transformers
We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention.
Ranked #4 on
Action Recognition
on Diving-48
Future Transformer for Long-term Action Anticipation
The task of predicting future actions from a video is crucial for a real-world agent interacting with others.
Relational Self-Attention: What's Missing in Attention for Video Understanding
Convolution has been arguably the most important feature transform for modern neural networks, leading to the advance of deep learning.
Ranked #11 on
Action Recognition
on Diving-48
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition
With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition.
Ranked #18 on
Action Recognition
on Something-Something V1
(using extra training data)
Learning Self-Similarity in Space and Time as a Generalized Motion for Action Recognition
We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it.
MotionSqueeze: Neural Motion Feature Learning for Video Understanding
As the frame-by-frame optical flows require heavy computation, incorporating motion information has remained a major computational bottleneck for video understanding.
Ranked #1 on
Video Classification
on Something-Something V2
