Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model
This work addresses a new benchmark for human action adverb recognition, which is an incremental step in computer vision towards more AI-like understanding.
The authors tackled the problem of recognizing human action adverbs by introducing the ADHA dataset and a three-stream hybrid model, achieving improved results over existing methods.
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.