Multi-Attribute Vision Transformers are Efficient and Robust Learners

• URL: http://arxiv.org/abs/2402.08070v2
• Date: Fri, 19 Jul 2024 16:51:02 GMT
• Title: Multi-Attribute Vision Transformers are Efficient and Robust Learners
• Authors: Hanan Gani, Nada Saadi, Noor Hussein, Karthik Nandakumar,
• Abstract summary: Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) We present a straightforward yet effective strategy for training various attributes through a single ViT network as distinct tasks. We assess the resilience of multi-attribute ViTs against adversarial attacks and compare their performance against ViTs designed for single attributes.
• Score: 4.53923275658276
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Since their inception, Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) across a wide spectrum of tasks. ViTs exhibit notable characteristics, including global attention, resilience against occlusions, and adaptability to distribution shifts. One underexplored aspect of ViTs is their potential for multi-attribute learning, referring to their ability to simultaneously grasp multiple attribute-related tasks. In this paper, we delve into the multi-attribute learning capability of ViTs, presenting a straightforward yet effective strategy for training various attributes through a single ViT network as distinct tasks. We assess the resilience of multi-attribute ViTs against adversarial attacks and compare their performance against ViTs designed for single attributes. Moreover, we further evaluate the robustness of multi-attribute ViTs against a recent transformer based attack called Patch-Fool. Our empirical findings on the CelebA dataset provide validation for our assertion. Our code is available at https://github.com/hananshafi/MTL-ViT

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