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Deep neural networks for classification of videos, just like image classification networks, may be subjected to adversarial manipulation.
Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group.
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.
We benchmark contemporary action recognition models (TSN, TRN, and TSM) on the recently introduced EPIC-Kitchens dataset and release pretrained models on GitHub (https://github. com/epic-kitchens/action-models) for others to build upon.
Most state-of-the-art methods for action recognition consist of a two-stream architecture with 3D convolutions: an appearance stream for RGB frames and a motion stream for optical flow frames.
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
SOTA for Multi-Task Learning on HVU
It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks.
Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube.
Weakly supervised object detection aims at reducing the amount of supervision required to train detection models.
Deep learning approaches have been established as the main methodology for video classification and recognition.