HHF: Hashing-guided Hinge Function for Deep Hashing Retrieval
This addresses retrieval accuracy issues in large-scale image retrieval for applications like search engines, but it is incremental as it builds on existing deep hashing methods.
The paper tackled the problem of semantic information loss during binarization in deep hashing for image retrieval, which damages accuracy, by proposing a Hashing-guided Hinge Function (HHF) to balance metric and quantization learning, resulting in consistent outperformance over existing techniques across multiple datasets like CIFAR-10, CIFAR-100, ImageNet, and MS-COCO.
Deep hashing has shown promising performance in large-scale image retrieval. However, latent codes extracted by Deep Neural Networks (DNNs) will inevitably lose semantic information during the binarization process, which damages the retrieval accuracy and makes it challenging. Although many existing approaches perform regularization to alleviate quantization errors, we figure out an incompatible conflict between metric learning and quantization learning. The metric loss penalizes the inter-class distances to push different classes unconstrained far away. Worse still, it tends to map the latent code deviate from ideal binarization point and generate severe ambiguity in the binarization process. Based on the minimum distance of the binary linear code, we creatively propose Hashing-guided Hinge Function (HHF) to avoid such conflict. In detail, the carefully-designed inflection point, which relies on the hash bit length and category numbers, is explicitly adopted to balance the metric term and quantization term. Such a modification prevents the network from falling into local metric optimal minima in deep hashing. Extensive experiments in CIFAR-10, CIFAR-100, ImageNet, and MS-COCO show that HHF consistently outperforms existing techniques, and is robust and flexible to transplant into other methods. Code is available at https://github.com/JerryXu0129/HHF.