Fine-Grained Alignment Network for Zero-Shot Cross-Modal Retrieval

Abstract

Zero-Shot Cross-Modal Retrieval (ZS-CMR) aims to perform cross-modal retrieval on data of unseen classes, where a key challenge is how to address the modality-gap and domain-shift problems simultaneously. Existing methods tackle this challenge mainly by embracing a sample-label alignment paradigm, which aligns samples of different modalities but of the same class with the word embedding of their class label. However, these methods only focus on the class-level alignment and overlook the alignment of rich fine-grained semantic information in samples, incurring coarse understanding of sample matching and poor generalization on unseen classes. In this article, we propose a novel Fine-Grained Alignment Network, an end-to-end framework that learns representation with two fine-grained alignment strategies, yielding representation space that can be better generalized to unseen classes. Specifically, we extract two kinds of fine-grained representations, region embedding and label distribution, respectively, from aspects of both feature and label. To optimize the region embedding, we propose a Fine-Grained Contrastive Learning (FGCL) strategy to simultaneously conduct class-level alignment and model the intra-class discrepancy. To optimize the label distribution, we propose a Fine-Grained Label Alignment (FGLA) strategy to align diverse fine-grained semantic information among samples, rather than merely label information. Finally, both region embedding and label distribution are utilized together to perform ZS-CMR at a finer granularity. Experimental results on three widely used datasets demonstrate that our method outperforms the state-of-the-art methods by a large margin. Detailed ablation studies have also been carried out, which provably affirm the advantage of each component we propose. Our code will be available at https://github.com/ShipingGe/FGAN .