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• W4386075952 abstract "In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+l model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for Vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://github.com/BrandonHanx/FAME-ViL." @default.
• W4386075952 created "2023-08-23" @default.
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• W4386075952 date "2023-06-01" @default.
• W4386075952 modified "2023-10-17" @default.
• W4386075952 title "FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks" @default.
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• W4386075952 doi "https://doi.org/10.1109/cvpr52729.2023.00262" @default.
• W4386075952 hasPublicationYear "2023" @default.
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