Group Generalized Mean Pooling for Vision Transformer

TL;DR

This paper introduces Group Generalized Mean (GGeM) pooling for Vision Transformers, which improves feature aggregation by considering channel groups, leading to performance boosts across classification, retrieval, and multi-modal tasks.

Contribution

The paper proposes GGeM pooling, a novel channel grouping strategy for ViT that enhances feature aggregation and outperforms existing pooling methods.

Findings

GGeM improves classification accuracy on ImageNet-1K by 0.1%-0.7%.

GGeM outperforms existing pooling strategies in image retrieval tasks.

GGeM is simple to implement with minimal code modifications.

Abstract

Vision Transformer (ViT) extracts the final representation from either class token or an average of all patch tokens, following the architecture of Transformer in Natural Language Processing (NLP) or Convolutional Neural Networks (CNNs) in computer vision. However, studies for the best way of aggregating the patch tokens are still limited to average pooling, while widely-used pooling strategies, such as max and GeM pooling, can be considered. Despite their effectiveness, the existing pooling strategies do not consider the architecture of ViT and the channel-wise difference in the activation maps, aggregating the crucial and trivial channels with the same importance. In this paper, we present Group Generalized Mean (GGeM) pooling as a simple yet powerful pooling strategy for ViT. GGeM divides the channels into groups and computes GeM pooling with a shared pooling parameter per group. As ViT groups the channels via a multi-head attention mechanism, grouping the channels by GGeM leads to lower head-wise dependence while amplifying important channels on the activation maps. Exploiting GGeM shows 0.1%p to 0.7%p performance boosts compared to the baselines and achieves state-of-the-art performance for ViT-Base and ViT-Large models in ImageNet-1K classification task. Moreover, GGeM outperforms the existing pooling strategies on image retrieval and multi-modal representation learning tasks, demonstrating the superiority of GGeM for a variety of tasks. GGeM is a simple algorithm in that only a few lines of code are necessary for implementation.