Abstract
We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers (BERT) in the pre-training model, making MVLT the first end-to-end framework for the fashion domain. Besides, we designed masked image reconstruction (MIR) for a fine-grained understanding of fashion. MVLT is an extensible and convenient architecture that admits raw multi-modal inputs without extra pre-processing models (e.g., ResNet), implicitly modeling the vision-language alignments. More importantly, MVLT can easily generalize to various matching and generative tasks. Experimental results show obvious improvements in retrieval (rank@5: 17%) and recognition (accuracy: 3%) tasks over the Fashion-Gen 2018 winner, Kaleido-BERT. The code is available at https://github.com/GewelsJI/MVLT.
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This work is funded by Toyota Motor Europe via the research project TRACE-Zürich. The authors also would like to thank the anonymous reviewers and editor for their helpful comments on this manuscript.
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The authors declared that they have no conflicts of interest in this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Work was done while Ge-Peng Ji was a research intern at Alibaba Group.
Ge-Peng Ji received the M. Sc. degree in communication and information systems from Wuhan University, China in 2021. He is currently a Ph.D. degree candidate at Australian National University, supervised by Professor Nick Barnes, majoring in engineering and computer science. He has published about 10 peer-reviewed journal and conference papers. In 2021, he received the Student Travel Award from Medical Image Computing and Computer-assisted Intervention Society.
His research interests lie in computer vision, especially in a variety of dense prediction tasks, such as video analysis, medical image segmentation, camouflaged object segmentation, and saliency detection.
Mingchen Zhuge received the M. Sc. degree in computer science from China University of Geosciences, China in 2021. He is a Ph. D. degree candidate in King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Juergen Schmidhuber. In 2019, he won the championship in the ZTE algorithm competition. He has worked as an intern at Alibaba Group and IIAI, as well as a visiting scholar at SUSTech. Besides, he has been invited to serve as a top conference reviewer for CVPR, ICML, ECCV, NeurIPS, etc.
His research interests include multi-modal learning and reinforcement learning.
Dehong Gao received the Ph. D. degree from The Hong Kong Polytechnic University, China in 2014. He is now working as an associate professor in Northwestern Polytechnical University, China.
His research interests include information retrieval, recommendation, natural language processing and machine learning.
Deng-Ping Fan received the Ph. D. degree from the Nankai University, China in 2019. He joined Inception Institute of Artificial Intelligence (IIAI), UAE in 2019. He has published about 50 top journal and conference papers such as TPAMI, IJCV, TIP, TNNLS, TMI, CVPR, ICCV, ECCV, IJCAI, etc. He won the Best Paper Finalist Award at IEEE CVPR 2019, the Best Paper Award Nominee at IEEE CVPR 2020. He was recognized as the CVPR 2019 outstanding reviewer with a special mention award, the CVPR 2020 outstanding reviewer, the ECCV 2020 high-quality reviewer, and the CVPR 2021 outstanding reviewer. He served as a program committee board (PCB) member of IJCAI 2022–2024, a senior program committee (SPC) member of IJCAI 2021, a program committee members (PC) of CAD&CG 2021, a committee member of China Society of Image and Graphics (CSIG), area chair in NeurIPS 2021 Datasets and Benchmarks Track, area chair in MICCAI2020 Workshop.
His research interests include computer vision, deep learning, and visual attention, especially the human vision on co-salient
object detection, RGB salient object detection, RGB-D salient object detection, and video salient object detection.
Christos Sakaridis received the M. Sc. degree in computer science from ETH Zürich, Switzerland in 2016 and his Diploma in electrical and computer engineering from the National Technical University of Athens, Greece in 2014, conducting his Diploma thesis at CVSP Group under the supervision of Prof. Petros Maragos. He received the Ph.D. degree in electrical engineering and information technology from ETH Zürich, Switzerland in 2021, working at Computer Vision Lab and supervised by Prof. Luc Van Gool. He is a postdoctoral researcher at Computer Vision Lab, ETH Zürich, Switzerland. Since 2021, he is the Principal Engineer in TRACE-Zürich, a project on computer vision for autonomous cars running at Computer Vision Lab and funded by Toyota Motor Europe. Moreover, he is the team leader in the EFCL project Sensor Fusion, in which they develop adaptive sensor fusion architectures for high-level visual perception.
His broad research fields are computer vision and machine learning. The focus of his research is on high-level visual perception, involving adverse visual conditions, domain adaptation, semantic segmentation, depth estimation, object detection, synthetic data generation, and fusion of multiple sensors (including lidar, radar and event cameras, with emphasis on their application to autonomous cars and robots).
Luc Van Gool received the Ph. D. degree in electromechanical engineering at Katholieke Universiteit Leuven, Belgium in 1981. Currently, he is a professor at Katholieke Universiteit Leuven, in Belgium and ETH Zürich, Switzerland. He leads computer vision research at both places and also teaches at both. He has been a program committee member of several major computer vision conferences. He received several Best Paper awards, won a David Marr Prize and a Koenderink Award, and was nominated Distinguished Researcher by the IEEE Computer Science committee. He is a co-founder of 10 spin-off companies.
His interests include 3D reconstruction and modeling, object recognition, tracking, gesture analysis, and a combination of those.
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Ji, GP., Zhuge, M., Gao, D. et al. Masked Vision-language Transformer in Fashion. Mach. Intell. Res. 20, 421–434 (2023). https://doi.org/10.1007/s11633-022-1394-4
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DOI: https://doi.org/10.1007/s11633-022-1394-4
Keywords
- Vision-language
- masked image reconstruction
- transformer
- fashion
- e-commercial