![]() Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. A novel hybrid attention network is proposed for the task. Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. Experimental results show that our approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric. ![]() To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. ![]()
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