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Found 9 papers, 3 papers with code
Learning Higher-order Object Interactions for Keypoint-based Video Understanding
Specifically, KeyNet introduces the use of object based keypoint information to capture context in the scene.
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects.
Ranked #2 on
Group Activity Recognition
on Collective Activity
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
We evaluate over CATER dataset and find that Hopper achieves 73. 2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames.
Ranked #5 on
Video Object Tracking
on CATER
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e. g., videos and audios) under self-supervision.
15 Keypoints Is All You Need
Keypoints are tracked using our Pose Entailment method, in which, first, a pair of pose estimates is sampled from different frames in a video and tokenized.
Ranked #2 on
Pose Tracking
on PoseTrack2017
Tripping through time: Efficient Localization of Activities in Videos
Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video.
Grounded Objects and Interactions for Video Captioning
We address the problem of video captioning by grounding language generation on object interactions in the video.
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Human actions often involve complex interactions across several inter-related objects in the scene.
Pruning Filters for Efficient ConvNets
However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks.
Ranked #1 on
Network Pruning
on ImageNet
