P2T: Pyramid Pooling Transformer for Scene Understanding

• URL: http://arxiv.org/abs/2106.12011v1
• Date: Tue, 22 Jun 2021 18:28:52 GMT
• Title: P2T: Pyramid Pooling Transformer for Scene Understanding
• Authors: Yu-Huan Wu, Yun Liu, Xin Zhan, Ming-Ming Cheng
• Abstract summary: We build a downstream-task-oriented transformer network, dubbed Pyramid Pooling Transformer (P2T) Plugged with our pooling-based MHSA, we build a downstream-task-oriented transformer network, dubbed Pyramid Pooling Transformer (P2T)
• Score: 62.41912463252468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper jointly resolves two problems in vision transformer: i) the computation of Multi-Head Self-Attention (MHSA) has high computational/space complexity; ii) recent vision transformer networks are overly tuned for image classification, ignoring the difference between image classification (simple scenarios, more similar to NLP) and downstream scene understanding tasks (complicated scenarios, rich structural and contextual information). To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful context abstraction, and its natural property of spatial invariance is suitable to address the loss of structural information (problem ii)). Hence, we propose to adapt pyramid pooling to MHSA for alleviating its high requirement on computational resources (problem i)). In this way, this pooling-based MHSA can well address the above two problems and is thus flexible and powerful for downstream scene understanding tasks. Plugged with our pooling-based MHSA, we build a downstream-task-oriented transformer network, dubbed Pyramid Pooling Transformer (P2T). Extensive experiments demonstrate that, when applied P2T as the backbone network, it shows substantial superiority in various downstream scene understanding tasks such as semantic segmentation, object detection, instance segmentation, and visual saliency detection, compared to previous CNN- and transformer-based networks. The code will be released at https://github.com/yuhuan-wu/P2T. Note that this technical report will keep updating.

Related papers

• Adaptive Step-size Perception Unfolding Network with Non-local Hybrid Attention for Hyperspectral Image Reconstruction [0.39134031118910273]
  We propose an adaptive step-size perception unfolding network (ASPUN), a deep unfolding network based on FISTA algorithm. In addition, we design a Non-local Hybrid Attention Transformer(NHAT) module for fully leveraging the receptive field advantage of transformer. Experimental results show that our ASPUN is superior to the existing SOTA algorithms and achieves the best performance.
  arXiv  Detail & Related papers  (2024-07-04T16:09:52Z)
• CT-MVSNet: Efficient Multi-View Stereo with Cross-scale Transformer [8.962657021133925]
  Cross-scale transformer (CT) processes feature representations at different stages without additional computation. We introduce an adaptive matching-aware transformer (AMT) that employs different interactive attention combinations at multiple scales. We also present a dual-feature guided aggregation (DFGA) that embeds the coarse global semantic information into the finer cost volume construction.
  arXiv  Detail & Related papers  (2023-12-14T01:33:18Z)
• Dual-Tasks Siamese Transformer Framework for Building Damage Assessment [11.888964682446879]
  We present the first attempt at designing a Transformer-based damage assessment architecture (DamFormer) To the best of our knowledge, it is the first time that such a deep Transformer-based network is proposed for multitemporal remote sensing interpretation tasks.
  arXiv  Detail & Related papers  (2022-01-26T14:11:16Z)
• Augmenting Convolutional networks with attention-based aggregation [55.97184767391253]
  We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning. We plug this learned aggregation layer with a simplistic patch-based convolutional network parametrized by 2 parameters (width and depth) It yields surprisingly competitive trade-offs between accuracy and complexity, in particular in terms of memory consumption.
  arXiv  Detail & Related papers  (2021-12-27T14:05:41Z)
• PnP-DETR: Towards Efficient Visual Analysis with Transformers [146.55679348493587]
  Recently, DETR pioneered the solution vision tasks with transformers, it directly translates the image feature map into the object result. Recent transformer-based image recognition model andTT show consistent efficiency gain.
  arXiv  Detail & Related papers  (2021-09-15T01:10:30Z)
• Less is More: Pay Less Attention in Vision Transformers [61.05787583247392]
  Less attention vIsion Transformer builds upon the fact that convolutions, fully-connected layers, and self-attentions have almost equivalent mathematical expressions for processing image patch sequences. The proposed LIT achieves promising performance on image recognition tasks, including image classification, object detection and instance segmentation.
  arXiv  Detail & Related papers  (2021-05-29T05:26:07Z)
• Transformers Solve the Limited Receptive Field for Monocular Depth Prediction [82.90445525977904]
  We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
  arXiv  Detail & Related papers  (2021-03-22T18:00:13Z)
• Scalable Visual Transformers with Hierarchical Pooling [61.05787583247392]
  We propose a Hierarchical Visual Transformer (HVT) which progressively pools visual tokens to shrink the sequence length. It brings a great benefit by scaling dimensions of depth/width/resolution/patch size without introducing extra computational complexity. Our HVT outperforms the competitive baselines on ImageNet and CIFAR-100 datasets.
  arXiv  Detail & Related papers  (2021-03-19T03:55:58Z)
• Toward Transformer-Based Object Detection [12.704056181392415]
  Vision Transformers can be used as a backbone by a common detection task head to produce competitive COCO results. ViT-FRCNN demonstrates several known properties associated with transformers, including large pretraining capacity and fast fine-tuning performance. We view ViT-FRCNN as an important stepping stone toward a pure-transformer solution of complex vision tasks such as object detection.
  arXiv  Detail & Related papers  (2020-12-17T22:33:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.