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Quo Vadis, Skeleton Action Recognition ?
In this paper, we study current and upcoming frontiers across the landsc...
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FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge
In this report we describe the technical details of our submission to th...
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Revisiting hand-crafted feature for action recognition: a set of improved dense trajectories
We propose a feature for action recognition called Trajectory-Set (TS), ...
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Towards Improving Spatiotemporal Action Recognition in Videos
Spatiotemporal action recognition deals with locating and classifying ac...
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MSC: A Dataset for Macro-Management in StarCraft II
Macro-management is an important problem in StarCraft, which has been st...
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Action Recognition Using Volumetric Motion Representations
Traditional action recognition models are constructed around the paradig...
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FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
We present FewRel 2.0, a more challenging task to investigate two aspect...
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An Evaluation of Action Recognition Models on EPIC-Kitchens
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. In contrast to popular action recognition datasets like Kinetics, Something-Something, UCF101, and HMDB51, EPIC-Kitchens is shot from an egocentric perspective and captures daily actions in-situ. In this report, we aim to understand how well these models can tackle the challenges present in this dataset, such as its long tail class distribution, unseen environment test set, and multiple tasks (verb, noun and, action classification). We discuss the models' shortcomings and avenues for future research.
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