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arXiv · 2108.03662

Discriminative Latent Semantic Graph for Video Captioning

Yang Bai·Junyan Wang·Yang Long·Bingzhang Hu·Yang Song·Maurice Pagnucco·Yu Guan
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Abstract

Video captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video. Existing generative models like encoder-decoder frameworks cannot explicitly explore the object-level interactions and frame-level information from complex spatio-temporal data to generate semantic-rich captions. Our main contribution is to identify three key problems in a joint framework for future video summarization tasks. 1) Enhanced Object Proposal: we propose a novel Conditional Graph that can fuse spatio-temporal information into latent object proposal. 2) Visual Knowledge: Latent Proposal Aggregation is proposed to dynamically extract visual words with higher semantic levels. 3) Sentence Validation: A novel Discriminative Language Validator is proposed to verify generated captions so that key semantic concepts can be effectively preserved. Our experiments on two public datasets (MVSD and MSR-VTT) manifest significant improvements over state-of-the-art approaches on all metrics, especially for BLEU-4 and CIDEr. Our code is available at https://github.com/baiyang4/D-LSG-Video-Caption.

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Yang Bai, Junyan Wang, Yang Long, Bingzhang Hu, Yang Song, Maurice Pagnucco, Yu Guan. 2021-08-08. Discriminative Latent Semantic Graph for Video Captioning. https://doi.org/10.1145/3474085.3475519

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