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      Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model

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          Abstract

          We introduce the first benchmark for a new problem --- recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). This is the first step for computer vision to change over from pattern recognition to real AI. We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labeling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results show that such methods are unsatisfactory. Moreover, we propose a novel three-stream hybrid model to deal the HAA problem, which achieves a better result.

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          ImageNet: A large-scale hierarchical image database

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            HMDB: A large video database for human motion recognition

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              Learning realistic human actions from movies

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                Author and article information

                Journal
                04 February 2018
                Article
                1802.01144
                d8ee78a0-5e92-4656-970a-c40751413114

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CV

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