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For the multi-label image retrieval, the existing hashing algorithms neglect the dependency between objects and thus fail to capture the attention information in the feature extraction, which affects the precision of hash codes. To address this problem, we explore the inter-dependency between objects through their co-occurrence correlation from the label set and adopt Multi-modal Factorized Bilinear (MFB) pooling component so that the image representation learning can capture this attention information. We propose a Label-Attended Hashing (LAH) algorithm which enables an end-to-end hash model with inter-dependency feature extraction. LAH first combines Convolutional Neural Network (CNN) and Graph Convolution Network (GCN) to separately generate the image representation and label co-occurrence embeddings, then adopts MFB to fuse these two modal vectors, finally learns the hash function with a Cauchy distribution based loss function via back propagation. Extensive experiments on public multi-label datasets demonstrate that (1) LAH can achieve the state-of-the-art retrieval results and (2) the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning. GitHub address: https://github.com/IDSM-AI/LAH.

In multi-label image recognition, it has become a popular method to predict those labels that co-occur in an image via modeling the label dependencies. Previous works focus on capturing the correlation between labels, but neglect to effectively fuse the image features and label embeddings, which severely affects the convergence efficiency of the model and inhibits the further precision improvement of multi-label image recognition. To overcome this shortcoming, in this paper, we introduce Multi-modal Factorized Bilinear pooling (MFB) which works as an efficient component to fuse cross-modal embeddings and propose F-GCN, a fast graph convolution network (GCN) based multi-label image recognition model. F-GCN consists of three key modules: (1) an image representation learning module which adopts a convolution neural network (CNN) to learn and generate image representations, (2) a label co-occurrence embedding module which first obtains the label vectors via the word embeddings technique and then adopts GCN to capture label co-occurrence embeddings and (3) an MFB fusion module which efficiently fuses these cross-modal vectors to enable an end-to-end model with a multi-label loss function. We conduct extensive experiments on two multi-label datasets including MS-COCO and VOC2007. Experimental results demonstrate the MFB component efficiently fuses image representations and label co-occurrence embeddings and thus greatly improves the convergence efficiency of the model. In addition, the performance of image recognition has also been promoted compared with the state-of-the-art methods. CCS CONCEPTS • Computing methodologies → Image representations.