An Adversarial Approach To Hard Triplet Generation

Keywords

Adversarial nets; Hard examples; Image retrieval

Abstract

While deep neural networks have demonstrated competitive results for many visual recognition and image retrieval tasks, the major challenge lies in distinguishing similar images from different categories (i.e., hard negative examples) while clustering images with large variations from the same category (i.e., hard positive examples). The current state-of-the-art is to mine the most hard triplet examples from the mini-batch to train the network. However, mining-based methods tend to look into these triplets that are hard in terms of the current estimated network, rather than deliberately generating those hard triplets that really matter in globally optimizing the network. For this purpose, we propose an adversarial network for Hard Triplet Generation (HTG) to optimize the network ability in distinguishing similar examples of different categories as well as grouping varied examples of the same categories. We evaluate our method on the real-world challenging datasets, such as CUB200-2011, CARS196, DeepFashion and VehicleID datasets, and show that our method outperforms the state-of-the-art methods significantly.

Publication Date

1-1-2018

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11213 LNCS

Number of Pages

508-524

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-030-01240-3_31

Socpus ID

85055132608 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85055132608

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