In this paper, we propose a multimodal search engine that combines visual and textual cues to retrieve items from a multimedia database aesthetically similar to the query. The goal of our engine is to enable intuitive retrieval of fashion merchandise such as clothes or furniture. Existing search engines treat textual input only as an additional source of information about the query image and do not correspond to the real-life scenario where the user looks for 'the same shirt but of denim'. Our novel method, dubbed DeepStyle, mitigates those shortcomings by using a joint neural network architecture to model contextual dependencies between features of different modalities. We prove the robustness of this approach on two different challenging datasets of fashion items and furniture where our DeepStyle engine outperforms baseline methods by 18-21 on the tested datasets. Our search engine is commercially deployed and available through a Web-based application.
@cite_6 present an encoder-decoder pipeline that learns a joint multimodal embedding (VSE) from images and a text, which is later used to generate text captions for custom images. Their approach is inspired by successes in Neural Machine Translation (NMT) and perceives visual and textual modalities as the same concept described in different languages. The proposed architecture consists of LSTM RNNs for encoding sentences, CNN for encoding images and structure-content neural language model (SC-NLM) for decoding. The authors show that their learned multimodal embedding space preserves semantic regularities in terms of vector space arithmetic e.g. image of a blue car - "blue" + "red" is near images of red cars. However, results of this task are only available in some example images. We would like to leverage their work and numerically evaluate multimodal query retrieval, specifically in the domain of fashion and interior design.
@cite_6
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Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* - "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.