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.
In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general. Code, data and models are publicly available  This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a largemargin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and textto-image retrieval. Our method achieves new state-of-theart results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase localization on the Flickr30K Entities dataset. 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. In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.