Cross-modal Image Retrieval with Deep Mutual Information Maximization

by   Chunbin Gu, et al.

In this paper, we study the cross-modal image retrieval, where the inputs contain a source image plus some text that describes certain modifications to this image and the desired image. Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs; 2) fuse the feature of the source image and its modified text to obtain fusion feature; 3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning. Since classical image/text encoders can learn the useful representation and common pair-based loss functions of distance metric learning are enough for cross-modal retrieval, people usually improve retrieval accuracy by designing new fusion networks. However, these methods do not successfully handle the modality gap caused by the inconsistent distribution and representation of the features of different modalities, which greatly influences the feature fusion and similarity learning. To alleviate this problem, we adopt the contrastive self-supervised learning method Deep InforMax (DIM) to our approach to bridge this gap by enhancing the dependence between the text, the image, and their fusion. Specifically, our method narrows the modality gap between the text modality and the image modality by maximizing mutual information between their not exactly semantically identical representation. Moreover, we seek an effective common subspace for the semantically same fusion feature and desired image's feature by utilizing Deep InforMax between the low-level layer of the image encoder and the high-level layer of the fusion network. Extensive experiments on three large-scale benchmark datasets show that we have bridged the modality gap between different modalities and achieve state-of-the-art retrieval performance.


page 18

page 21


On Metric Learning for Audio-Text Cross-Modal Retrieval

Audio-text retrieval aims at retrieving a target audio clip or caption f...

A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing

Due to the availability of multi-modal remote sensing (RS) image archive...

Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval

Text-Pedestrian Image Retrieval aims to use the text describing pedestri...

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Thanks to the success of deep learning, cross-modal retrieval has made s...

Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval

Accurately matching visual and textual data in cross-modal retrieval has...

Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval

Due to the large cross-modality discrepancy between 2D sketches and 3D s...

Modality-dependent Cross-media Retrieval

In this paper, we investigate the cross-media retrieval between images a...

Please sign up or login with your details

Forgot password? Click here to reset