P2T: Pyramid Pooling Transformer for Scene Understanding

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 45; no. 11; pp. 12760 - 12771
• Main Authors: Wu, Yu-Huan, Liu, Yun, Zhan, Xin, Cheng, Ming-Ming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: backbone network; Computational modeling; Computer networks; Convolution; efficient self-attention; Feature extraction; Image classification; Image segmentation; Network design; Object recognition; pyramid pooling; Scene analysis; scene understanding; Semantic segmentation; Semantics; Task analysis; Transformer; Transformers
• ISSN: 0162-8828; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2022.3202765

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 .