Correlation Hashing Network for Efficient Cross-Modal Retrieval
This work addresses efficiency and accuracy issues in large-scale multimodal retrieval for applications like image-text search, though it is incremental as it builds on existing deep hashing methods.
The paper tackles the problem of suboptimal hash codes in cross-modal retrieval by introducing a Correlation Hashing Network (CHN) that jointly optimizes feature representation and quantization error, resulting in state-of-the-art performance on standard benchmarks.
Hashing is widely applied to approximate nearest neighbor search for large-scale multimodal retrieval with storage and computation efficiency. Cross-modal hashing improves the quality of hash coding by exploiting semantic correlations across different modalities. Existing cross-modal hashing methods first transform data into low-dimensional feature vectors, and then generate binary codes by another separate quantization step. However, suboptimal hash codes may be generated since the quantization error is not explicitly minimized and the feature representation is not jointly optimized with the binary codes. This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error. The proposed CHN is a hybrid deep architecture that constitutes a convolutional neural network for learning good image representations, a multilayer perception for learning good text representations, two hashing layers for generating compact binary codes, and a structured max-margin loss that integrates all things together to enable learning similarity-preserving and high-quality hash codes. Extensive empirical study shows that CHN yields state of the art cross-modal retrieval performance on standard benchmarks.