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Texture BERT for Cross-modal Texture Image Retrieval by Zelai Xu; Tan Yu; Ping Li is a scholarly article available to read on EtoBox.

What is Texture BERT for Cross-modal Texture Image Retrieval about?

We propose Texture BERT, a model describing visual attributes of texture using natural language. To capture the rich details in texture images, we propose a group-wise compact bilinear pooling method, which represents the texture image by a set of visual patterns. The similarity between the texture image and the corresponding language description is determined by the cross-matching between the set of visual patterns from the texture image and the set of word features from the language description. We also exploit the selfattention transformer layers to provide the cross-modal context and enhance the effectiveness of matching. Our efforts achieve stateof-the-art accuracy on both text retrieval and image retrieval tasks, demonstrating the effectiveness of the proposed Texture BERT model in describing texture through natural language. CCS CONCEPTS • Information systems → Multimedia and multimodal retrieval.

Author

Zelai Xu; Tan Yu; Ping Li

Publisher

ACM

Published

2022

Language

EN