Batra, V ORCID: https://orcid.org/0000-0001-9049-5542, Haldar, A, He, Y, Ferhatosmanoglu, H, Vogiatzis, G and Guha, T (2020) Variational Recurrent sequence to sequence retrieval for stepwise illustration. Proceedings ofAdvances in Information Retrieval ECIR 2020, 12035. pp. 50-64.

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Abstract

We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods.

Item Type: Article
Additional Information: © Springer Nature Switzerland AG 2020 Information on the conference can be found at; https://link.springer.com/book/10.1007/978-3-030-45439-5 (42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part I) Any information required can be found from the above link.
Uncontrolled Keywords: Multimodal datasets, Semantics, Sequence retrieval
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 28 May 2020 08:41
Last Modified: 28 May 2020 08:41
URI: https://eprints.keele.ac.uk/id/eprint/8020

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