Learning Semantic-Aligned Feature Representation for Text-based Person Search

• URL: http://arxiv.org/abs/2112.06714v1
• Date: Mon, 13 Dec 2021 14:54:38 GMT
• Title: Learning Semantic-Aligned Feature Representation for Text-based Person Search
• Authors: Shiping Li, Min Cao, Min Zhang
• Abstract summary: We propose a semantic-aligned embedding method for text-based person search. The feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features. Experimental results on the CUHK-PEDES and Flickr30K datasets show that our method achieves state-of-the-art performances.
• Score: 8.56017285139081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based person search aims to retrieve images of a certain pedestrian by a textual description. The key challenge of this task is to eliminate the inter-modality gap and achieve the feature alignment across modalities. In this paper, we propose a semantic-aligned embedding method for text-based person search, in which the feature alignment across modalities is achieved by automatically learning the semantic-aligned visual features and textual features. First, we introduce two Transformer-based backbones to encode robust feature representations of the images and texts. Second, we design a semantic-aligned feature aggregation network to adaptively select and aggregate features with the same semantics into part-aware features, which is achieved by a multi-head attention module constrained by a cross-modality part alignment loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K datasets show that our method achieves state-of-the-art performances.

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