A Structured Listwise Approach to Learning to Rank for Image Tagging
| Autor: | Franco M. Luque, Jorge Sánchez, Leandro Lichtensztein |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information retrieval
Computer science Rank (computer programming) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Bilinear interpolation 02 engineering and technology Image (mathematics) Ranking (information retrieval) 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Relevance (information retrieval) Learning to rank |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030110239 ECCV Workshops (6) |
| Popis: | With the growing quantity and diversity of publicly available image data, computer vision plays a crucial role in understanding and organizing visual information today. Image tagging models are very often used to make this data accessible and useful. Generating image labels and ranking them by their relevance to the visual content is still an open problem. In this work, we use a bilinear compatibility function inspired from zero-shot learning that allows us to rank tags according to their relevance to the image content. We propose a novel listwise structured loss formulation to learn it from data. We leverage captioned image data and propose different “tags from captions” schemes meant to capture user attention and intra-user agreement in a simple and effective manner. We evaluate our method on the COCO-Captions, PASCAL-sentences and MIRFlickr-25k datasets showing promising results. |
| Databáze: | OpenAIRE |
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