In this paper, we propose to learn shared semantic space with correlation alignment ( @math ) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a neural network with convolutional layers and fully-connected layers to extract features for images, including images on Flickr-like social media. Simultaneously, we exploit a fully-connected neural network to extract semantic features for texts, including news articles from news media. In particular, nonlinear correlations of layer activations in the two neural networks are aligned with correlation alignment during the joint training of the networks. Furthermore, we project the multimodal data into a shared semantic space for cross-modal (event) retrieval, where the distances between heterogeneous data samples can be measured directly. In addition, we contribute a Wiki-Flickr Event dataset, where the multimodal data samples are not describing each other in pairs like the existing paired datasets, but all of them are describing semantic events. Extensive experiments conducted on both paired and unpaired datasets manifest the effectiveness of @math , outperforming the state-of-the-art methods.
The intuition of ranking models is that relevant pairs in the retrieved results should rank higher than the irrelevant pairs @cite_12 . Recently, neural networks combined with ranking loss become popular in the context of cross-modal retrieval. @cite_10 @cite_22 designed a two-branch neural network with multiple layers of linear projections followed by nonlinearities, and learned the joint embeddings for images and texts with a large margin objective which combines ranking constraints and neighborhood structure preserving constraints. @cite_33 introduced a large-scale recipe dataset, and jointly learned the embeddings of images and recipes in a common space by maximizing the cosine similarity between positive recipe-image pairs and minimizing the similarity between negative pairs. @cite_13 designed a sampling strategy and define discriminative ranking loss on two heterogeneous networks to obtain discriminative embeddings for cross-modal retrieval. However, the training of ranking loss relies on high-quality and unambiguous data pairs, which may not be appropriate for unpaired datasets, like our Wiki-Flickr Event dataset.
@cite_33 @cite_22 @cite_10 @cite_13 @cite_12
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Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image text group can be viewed as a class. So the network can learn the fine granularity from every image text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available. Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method. This paper considers the task of matching images and sentences. The challenge consists in discriminatively embedding the two modalities onto a shared visual-textual space. Existing work in this field largely uses Recurrent Neural Networks (RNN) for text feature learning and employs off-the-shelf Convolutional Neural Networks (CNN) for image feature extraction. Our system, in comparison, differs in two key aspects. Firstly, we build a convolutional network amenable for fine-tuning the visual and textual representations, where the entire network only contains four components, i.e., convolution layer, pooling layer, rectified linear unit function (ReLU), and batch normalisation. End-to-end learning allows the system to directly learn from the data and fully utilise the supervisions. Secondly, we propose instance loss according to viewing each multimodal data pair as a class. This works with a large margin objective to learn the inter-modal correspondence between images and their textual descriptions. Experiments on two generic retrieval datasets (Flickr30k and MSCOCO) demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language person retrieval, we improve the state of the art by a large margin. Code is available at this https URL com layumi Image-Text-Embedding This paper proposes a novel deep framework of multi-networks joint learning for large-scale cross-modal retrieval. For most existing cross-modal methods, the processes of training and testing don't care about the problem of memory requirement. Hence, they are generally implemented on small-scale data. Moreover, they take feature learning and latent space embedding as two separate steps which cannot generate specific features to accord with the cross-modal task. To alleviate the problems, we first disintegrate the multiplication and inverse of some big matrices, usually involved in existing methods, into that of many sub-matrices. Each sub-matrix is targeted to dispose one pair of image-sentence, for which we further design a novel sampling strategy to select the most representative samples to construct the cross-modal ranking loss and within-modal discriminant loss functions. By this way, the proposed model consumes less memory each time such that it can scale to large-scale data. Furthermore, we apply the proposed discriminative ranking loss to effectively unify two heterogenous networks, deep residual network for images and long short-term memory for sentences, into an end-to-end deep learning architecture. Finally, we can simultaneously achieve specific features adapting to cross-modal task and learn a shared latent space for images and sentences. Extensive evaluations on two large-scale cross-modal datasets show that the proposed method brings substantial improvements over other state-of-the-art ranking methods.