Language Person Search with Mutually Connected Classification Loss

In this work, we develop an effective person search algorithm with natural language descriptions. The contributions of this work mainly include two aspects. First, we design a baseline language person search framework including three basic components: a deep CNN model to extract visual features, a bi-directional LSTM to encode language descriptions and the triplet loss to conduct cross-modal feature embedding. Second, we propose a novel mutually connected classification loss to fully exploit the identity-level information, which not only introduces the identification information into both image and language descriptions but also encourages the cross-modal classification probabilities of the same identity to be more similar. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves significantly better performance than other state-of-the-art algorithms.