Creative domains render a big part of modern society, having a strong influence on the economy and cultural life. Much effort within creative domains, such as fashion, art and design, center around the creation, consumption, manipulation and analytics of visual content. In recent years, there has been an explosion of research in applying machine learning and computer vision algorithms to various aspects of the creative domains. For three years in a row, CVFAD workshop series have been capturing important trends and new ideas in this area. At CVPR 2020, CVFAD will continue to bring together artists, designers, and computer vision researchers and engineers. We will keep growing the workshop itself to be a space for conversations and idea exchanges at the intersection of computer vision and creative applications .
Similar to previous workshops, we will provide rich opportunities for the audience to participate, including two dataset challenges and a paper submission track.
- Due to issues with the Zoom meeting, we will not be having the panel.
- CVFAD 2020 is featured at the CVPR daily!
- Accepted papers are now online!
- We welcome Fashion IQ dataset challenge join our workshop! The previous Fashion IQ competition was at ICCV 2019: https://sites.google.com/view/lingir/fashion-iq
- We will not host the art gallery at this workshop. However, artworks from previous years are free online at: https://computervisionart.com
We solicit paper submissions on novel methods and application scenarios of CV and ML for creative applications. Areas of application include fashion, art, music, design, etc. We accept papers on a variety of topics, including generative models, retrieval, product recommendation, image segmentation, attribute discovery and trend forecast, etc. Accepted papers will be presented at the poster session and one paper will be awarded as the best paper.Please see the paper track page for more information. Please also note that our workshop doesn't have a proceeding.
We are proud to offer two fashion dataset challenges to encourage applying and developing new computer vision techniques for fashion retrieval.