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When you hear the word "dandy," you know you're talking about a sharp-dressed man. A woman would be more likely called a "clotheshorse" or "fashionista.

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Interactive image retrieval systems have been intensely studied in computer vision, with wide application domains such as Internet search, e-commerce and surveillance. Existing systems demonstrated the merit of incorporating fixed-form user feedback, such as indicating image relevance or visual attribute constraints. This challenge propelled the recent research interest combining visual and semantic information for interactive image search [2,3,4].

Especially, several new works demonstrated the potential of incorporating natural language input in image search [2,3,4]. Unmatched with the rising interest in this area, we observe a lack of natural, realistic dataset in this domain. Therefore, we present Fashion IQ dataset [1] and evolve our challenge, to push further research in this area and set up a fair progress measure.

The format of the challenge is similar to its version. Specifically, the challenge is an image retrieval task, where the input query is specified in the form of a candidate image and two natural language expressions which describe the visual differences of the search target.

For example, we may present an image of a white dress, and ask the system to find images which are in the same style and color, but are modified in small ways, such as adding a belt in the waist. The retrieval interface setup with natural language user input offers a natural and effective interface for image retrieval, serving as an important step towards developing full-blown conversational image search retrieval systems [5].

Please view the dataset here. Please refer to the paper to learn more details statistics, annotation procedure of the dataset.

The retrieval systems are evaluated by recall metrics on the test splits of the dataset. For each of the three fashion dresses, tops, shirtsRecall 10 and Recall 50 will be computed on all test queries. The overall performance is computed by taking the average of Recall 10 and Recall 50 on the three fashion .

Different from last year, side information attribute tags, Amazon product metadata, etc. Side information is considered as privileged information, only available during training. Therefore, only images and relative captions are allowed during testing.

The Codalab is now open. Please register through the website for your team and read carefully the instructions. NeurIPS CVPR Zhao, Bo, et al.

Saha, Amrita, Mitesh M. Khapra, and Karthik Sankaranarayanan. Search this site. Fashion IQ Report abuse. Google Sites.

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