The three existing dominant network families, i.e., CNNs, Transformers and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving designing more effective token-mixing mechanisms at the core of backbone architecture development. In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate contextual information from other tokens in the global scope into the given query token. This fundamental operator actively predicts where to capture useful contexts and learns how to fuse the captured contexts with the query token at channel level. In this way, the spatial range of token-mixing can be expanded to a global scope with limited computational complexity, where the way of token-mixing is reformed. We take ATMs as the primary operators and assemble them into a cascade architecture, dubbed ATMNet. Extensive experiments demonstrate that ATMNet is generally applicable and comprehensively surpasses different families of SOTA vision backbones by a clear margin on a broad range of vision tasks, including visual recognition and dense prediction tasks.  Code is available at https://github.com/microsoft/ActiveMLP.

Active Token Mixer

Data augmentation (DA) has been extensively studied to fa-
cilitate model optimization in many tasks. Prior DA works fo-
cus on designing augmentation operations themselves, while
leaving selecting suitable samples for augmentation out of
consideration. This might incur visual ambiguities and fur-
ther induce training biases. In this paper, we propose an ef-
fective approach, dubbed SelectAugment, to select samples
for augmentation in a deterministic and online manner based
on the sample contents and the network training status. To
facilitate the policy learning, in each batch, we exploit the
hierarchy of this task by first determining the augmenta-
tion ratio and then deciding whether to augment each train-
ing sample under this ratio. We model this process as two-
step decision-making and adopt Hierarchical Reinforcement
Learning (HRL) to learn the selection policy. In this way, the
negative effects of the randomness in selecting samples to
augment can be effectively alleviated and the effectiveness
of DA is improved. Extensive experiments demonstrate that
our proposed SelectAugment significantly improves various
off-the-shelf DA methods on image classification and fine-
grained image recognition.

SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

CV: Object Detection & Categorization 5

poster

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Zhizheng Zhang

2

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Active Token Mixer

SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

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