Negative-Aware Attention Framework for Image-Text Matching
Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang
Abstract
Image-text matching, as a fundamental task, bridges the gap between vision and language. The key of this task is to accurately measure similarity between these two modalities. Prior work measuring this similarity mainly based on matched fragments (i.e., word/region with high relevance), while underestimating or even ignoring the effect of mismatched fragments (i.e., word/region with low relevance), e.g., via a typical LeaklyReLU or ReLU operation that forces negative scores close or exact to zero in attention. This work argues that mismatched textual fragments, which contain rich mismatching clues, are also crucial for image-text matching. We thereby propose a novel Negative-Aware Attention Framework (NAAF), which explicitly exploits both the positive effect of matched fragments and the negative effect of mismatched fragments to jointly infer image-text similarity. NAAF (1) delicately designs an iterative optimization method to maximally mine the mismatched fragments, facilitating more discriminative and robust negative effects, and (2) devises the two-branch matching mechanism to precisely calculate similarity/dissimilarity degrees for matched/mismatched fragments with different masks. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superior effectiveness of our NAAF, achieving state-of-the-art performance. Code will be released at: https://github.com/CrossmodalGroup/NAAF.