P2T: Pyramid Pooling Transformer for Scene Understanding

Abstract

Recently, the vision transformer has achieved great success by pushing the state-of-the-art of various vision tasks. One of the most challenging problems in the vision transformer is that the large sequence length of image tokens leads to high computational cost (quadratic complexity). A popular solution to this problem is to use a single pooling operation to reduce the sequence length. This paper considers how to improve existing vision transformers, where the pooled feature extracted by a single pooling operation seems less powerful. To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful ability in context abstraction. However, pyramid pooling has not been explored in backbone network design. To bridge this gap, we propose to adapt pyramid pooling to Multi-Head Self-Attention (MHSA) in the vision transformer, simultaneously reducing the sequence length and capturing powerful contextual features. Plugged with our pooling-based MHSA, we build a universal vision transformer backbone, dubbed Pyramid Pooling Transformer (P2T). Extensive experiments demonstrate that, when applied P2T as the backbone network, it shows substantial superiority in various vision tasks such as image classification, semantic segmentation, object detection, and instance segmentation, compared to previous CNN- and transformer-based networks. The code will be released at https://github.com/yuhuan-wu/P2T.

Accepted by IEEE TPAMI

References in corpus (5)

• PVT v2: Improved Baselines with Pyramid Vision Transformer
• Conditional Positional Encodings for Vision Transformers
• LocalViT: Analyzing Locality in Vision Transformers
• MobileSal: Extremely Efficient RGB-D Salient Object Detection
• ISTR: End-to-End Instance Segmentation with Transformers

Cited by in corpus (7)

• Centralized Feature Pyramid for Object Detection
• MobileSal: Extremely Efficient RGB-D Salient Object Detection
• TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
• Revisiting Computer-Aided Tuberculosis Diagnosis
• Low-Resolution Self-Attention for Semantic Segmentation
• Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
• SERE: Exploring Feature Self-relation for Self-supervised Transformer