In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training. Due to dynamically increasing number of categories and practical constraint of training on every possible domain, which requires large amounts of data, generalizing to both unseen classes and domains is important. Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing. Specifically, we introduce a novel Semantic Neighborhood loss to bridge the knowledge gap between seen and unseen classes and ensure that the latent space embedding of the unseen classes is semantically meaningful with respect to its neighboring classes. We also introduce a mix-up based supervision at image-level as well as semantic-level of the data for training with the Mixture Prediction loss, which helps in efficient retrieval when the query belongs to an unseen domain. These losses are incorporated on the SE-ResNet50 backbone to obtain SnMpNet. Extensive experiments on two large-scale datasets, Sketchy Extended and DomainNet, and thorough comparisons with state-of-the-art justify the effectiveness of the proposed model.

The goal of zero-shot sketch-based image retrieval is to retrieve relevant images from a search set against a hand-drawn sketch query, which belongs to a class, previously unseen by the model. The knowledge gap between such unseen and seen classes along with the domain-gap between the query and search-set makes the problem extremely challenging. In this work, we address this problem by proposing a novel retrieval methodology,StyleGuideusing style-guidedfake-image generation. In addition, we further study the scenario of generalized zero-shot sketch-based image retrieval, where the search set contains images from both seen and unseen categories. Specifically, we propose a detection approach for unseen class samples in the search-set, based on pre-computed seen class-prototypes, to obtain a refined search-set for a particular unseen-class query. Thus, the query sketch needs to be compared only to those image data which are more likely to belong to the unseen classes, resulting in improved retrieval performance. Extensive experiments on two large-scale sketch-image datasets, Sketchy extended and TU-Berlin show that the proposed approach performs better or comparable to the state-of-the-art for ZS-SBIR and gives significant improvements over the state-of-the-art for generalized ZS-SBIR.

Sketch-based image retrieval (SBIR) is gaining increasing popularity because of its flexibility to search natural images using unrestricted hand-drawn sketch query. Here, we address a related, but relatively unexplored problem, where the users can also specify their preferred styles of the images they want to retrieve, e.g., color, shape, etc., as keywords, whose information is not present in the sketch. The contribution of this work is three-fold. First, we propose a deep network for the problem of style-augmented SBIR (or s-SBIR) having three main components - category module, style module and mixer module, which are trained in an end-to-end manner. Second, we propose a quintuplet loss, which takes into consideration both the category and style, while giving appropriate importance to the two components. Third, we propose a normalized composite evaluation metric or ncMAP which can quantitatively evaluate s-SBIR approaches. Extensive experiments on subsets of two benchmark image-sketch datasets, Sketchy and TU-Berlin show the effectiveness of the proposed approach.

Generative models have achieved impressive performance for the generalized zero-shot learning task by learning the mapping from attributes to feature space. In this work, we propose to derive semantic inferences from images and use them for the generation, which enables us to capture the bidirectional information i.e., visual to semantic and semantic to visual spaces. Specifically, we propose a Semantic Embedding module which not only gives image specific semantic information to the generative model for generation of better features, but also makes sure that the generated features can be mapped to the correct semantic space. We also propose an Integrated Classifier, which is trained along with the generator. This module not only eliminates the requirement of additional classifier for new object categories which is required by the existing generative approaches, but also facilitates the generation of more discriminative and useful features. This approach can be used seamlessly for the task of few-shot learning. Extensive experiments on four benchmark datasets, namely, CUB, SUN, AWA1, AWA2 for both zero-shot learning and few-shot setting show the effectiveness of the proposed approach.

Cross-modal retrieval is an important research area due to its wide range of applications, and several algorithms have been proposed to address this task. We feel that it is the right time to take a step back and analyze the current status of research in this area. As new object classes are continuously being discovered over time, it is necessary to design algorithms that can generalize to data from previously unseen classes. Towards that goal, our first contribution is to establish protocols for generalized zero-shot cross-modal retrieval and analyze the generalization ability of the standard cross-modal algorithms. Second, we propose a semantic-aware ranking algorithm that can be used as an add-on to any existing cross-modal approach to improve its performance on both seen and unseen classes. Finally, we propose a modification of the standard evaluation metric (MAP for single-label data and NDCG for multi-label data), which we feel is a more intuitive measure of the cross-modal retrieval performance. Extensive experiments on two single-label and three multi-label cross-modal datasets show the effectiveness of the proposed approach.