Identity-Aware Textual-Visual Matching with Latent Co-attention
This addresses the problem of improving accuracy in cross-modal retrieval for applications like image search, though it appears incremental by building on existing CNN-LSTM methods.
The paper tackles textual-visual matching by proposing an identity-aware two-stage framework with a novel loss and latent co-attention, achieving state-of-the-art performance with large margins on three datasets.
Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy (CMCE) loss. The stage-1 network is able to efficiently screen easy incorrect matchings and also provide initial training point for the stage-2 training. The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism. The spatial attention relates each word with corresponding image regions while the latent semantic attention aligns different sentence structures to make the matching results more robust to sentence structure variations. Extensive experiments on three datasets with identity-level annotations show that our framework outperforms state-of-the-art approaches by large margins.