Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks

• URL: http://arxiv.org/abs/2008.08012v1
• Date: Tue, 18 Aug 2020 16:29:49 GMT
• Title: Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks
• Authors: Gouthaman KV, Athira Nambiar, Kancheti Sai Srinivas, Anurag Mittal
• Abstract summary: We propose an attention mechanism - Linguistically-aware Attention (LAT) - that leverages object attributes obtained from generic object detectors. LAT represents visual and textual modalities in a common linguistically-rich space, thus providing linguistic awareness to the attention process. We apply and demonstrate the effectiveness of LAT in three Vision-language (V-L) tasks: Counting-VQA, VQA, and Image captioning.
• Score: 9.462808515258464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attention models are widely used in Vision-language (V-L) tasks to perform the visual-textual correlation. Humans perform such a correlation with a strong linguistic understanding of the visual world. However, even the best performing attention model in V-L tasks lacks such a high-level linguistic understanding, thus creating a semantic gap between the modalities. In this paper, we propose an attention mechanism - Linguistically-aware Attention (LAT) - that leverages object attributes obtained from generic object detectors along with pre-trained language models to reduce this semantic gap. LAT represents visual and textual modalities in a common linguistically-rich space, thus providing linguistic awareness to the attention process. We apply and demonstrate the effectiveness of LAT in three V-L tasks: Counting-VQA, VQA, and Image captioning. In Counting-VQA, we propose a novel counting-specific VQA model to predict an intuitive count and achieve state-of-the-art results on five datasets. In VQA and Captioning, we show the generic nature and effectiveness of LAT by adapting it into various baselines and consistently improving their performance.

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