Tell me what you see: A zero-shot action recognition method based on natural language descriptions

• URL: http://arxiv.org/abs/2112.09976v2
• Date: Mon, 11 Sep 2023 17:57:15 GMT
• Title: Tell me what you see: A zero-shot action recognition method based on natural language descriptions
• Authors: Valter Estevam and Rayson Laroca and David Menotti and Helio Pedrini
• Abstract summary: We propose using video captioning methods to extract semantic information from videos. To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences. We build a shared semantic space employing BERT-based embedders pre-trained in the paraphrasing task on multiple text datasets.
• Score: 3.136605193634262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel approach to Zero-Shot Action Recognition. Recent works have explored the detection and classification of objects to obtain semantic information from videos with remarkable performance. Inspired by them, we propose using video captioning methods to extract semantic information about objects, scenes, humans, and their relationships. To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences. More specifically, we represent videos using sentences generated via video captioning methods and classes using sentences extracted from documents acquired through search engines on the Internet. Using these representations, we build a shared semantic space employing BERT-based embedders pre-trained in the paraphrasing task on multiple text datasets. The projection of both visual and semantic information onto this space is straightforward, as they are sentences, enabling classification using the nearest neighbor rule. We demonstrate that representing videos and labels with sentences alleviates the domain adaptation problem. Additionally, we show that word vectors are unsuitable for building the semantic embedding space of our descriptions. Our method outperforms the state-of-the-art performance on the UCF101 dataset by 3.3 p.p. in accuracy under the TruZe protocol and achieves competitive results on both the UCF101 and HMDB51 datasets under the conventional protocol (0/50\% - training/testing split). Our code is available at https://github.com/valterlej/zsarcap.

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