The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks.
Ranked #2 on Action Recognition on UCF101
In this paper, we introduce a network architecture that takes long-term content into account and enables fast per-video processing at the same time.
Ranked #17 on Action Recognition on Something-Something V1 (using extra training data)
Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
Ranked #1 on Temporal Action Localization on THUMOS’14
To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes on the learned classification network to localize each action instance.
Ranked #1 on Temporal Action Localization on MEXaction2
This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales.
Ranked #6 on Semantic Segmentation on ADE20K val
Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art.
Ranked #1 on Action Classification on HMDB51
Acquiring spatio-temporal states of an action is the most crucial step for action classification.
Ranked #1 on Hand Gesture Recognition on Jester val