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Found 5 papers, 1 papers with code
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -
We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy attention in order to better learn the importance of features at each convolutional block of the network.
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
Adaptive Intermediate Representations for Video Understanding
A common strategy to video understanding is to incorporate spatial and motion information by fusing features derived from RGB frames and optical flow.
Ranked #5 on
Action Classification
on Toyota Smarthome dataset
AssembleNet++: Assembling Modality Representations via Attention Connections
We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy attention in order to better learn the importance of features at each convolutional block of the network.
Ranked #4 on
Action Classification
on Toyota Smarthome dataset
SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong
The corpus consists of ping pong play with three main annotation streams that can be used to learn tracking and action recognition models -- tracking of the ping pong ball and poses of humans in the videos and the spin of the ball being hit by humans.
