Unsupervised Adversarial Instance-Level Image Retrieval

With the wide use of visual sensors in the Internet of Things (IoT) in the past decades, huge amounts of images are captured in people's daily lives, which poses challenges to traditional deep-learning-based image retrieval frameworks. Most such frameworks need a large amount of annotated training data, which are expensive. Moreover, machines still lack human intelligence, as illustrated by the fact that they pay less attention to the interesting regions that humans generally focus on when searching for images. Hence, this paper proposes a novel unsupervised framework that focuses on the instance object in the image and integrates human intelligence into the deep-learning-based image retrieval. This framework is called adversarial instance-level image retrieval (AILIR). We incorporate adversarial training and an attention mechanism into this framework that considers human intelligence with artificial intelligence. The generator and discriminator are redesigned to guarantee that the generator retrieves similar images while the discriminator selects unmatched images and creates an adversarial reward for the generator. A minimax game is conducted by the adversarial reward retrieval mechanism until the discriminator is unable to judge whether the image sequence retrieved matches the query. Comparison and ablation experiments on four benchmark datasets prove that the proposed adversarial training framework indeed improves instance retrieval and outperforms the state-of-the-art methods focused on instance retrieval.