  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
