Transformer Meets DCFAM: A Novel Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images

• URL: http://arxiv.org/abs/2104.12137v1
• Date: Sun, 25 Apr 2021 11:34:22 GMT
• Title: Transformer Meets DCFAM: A Novel Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
• Authors: Libo Wang, Rui Li, Chenxi Duan, and Shenghui Fang
• Abstract summary: We introduce the Swin Transformer as the backbone to fully extract the context information. We also design a novel decoder named densely connected feature aggregation module (DCFAM) to restore the resolution and generate the segmentation map.
• Score: 6.171417925832851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fully-convolutional network (FCN) with an encoder-decoder architecture has become the standard paradigm for semantic segmentation. The encoder-decoder architecture utilizes an encoder to capture multi-level feature maps, which are then incorporated into the final prediction by a decoder. As the context is critical for precise segmentation, tremendous effort has been made to extract such information in an intelligent manner, including employing dilated/atrous convolutions or inserting attention modules. However, the aforementioned endeavors are all based on the FCN architecture with ResNet backbone which cannot tackle the context issue from the root. By contrast, we introduce the Swin Transformer as the backbone to fully extract the context information and design a novel decoder named densely connected feature aggregation module (DCFAM) to restore the resolution and generate the segmentation map. The extensive experiments on two datasets demonstrate the effectiveness of the proposed scheme.

Related papers

• More complex encoder is not all you need [0.882348769487259]
  We introduce neU-Net (i.e., not complex encoder U-Net), which incorporates a novel Sub-pixel Convolution for upsampling to construct a powerful decoder. Our model design achieves excellent results, surpassing other state-of-the-art methods on both the Synapse and ACDC datasets.
  arXiv  Detail & Related papers  (2023-09-20T08:34:38Z)
• LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention [0.0]
  We propose a projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic segmentation. The encoder is composed of a novel multi-scale convolutional attention (MSCA) module with varying receptive field sizes to capture features. We show that our proposed method is lighter, more efficient, and robust compared to state-of-the-art semantic segmentation methods.
  arXiv  Detail & Related papers  (2023-01-11T02:51:38Z)
• SoftPool++: An Encoder-Decoder Network for Point Cloud Completion [93.54286830844134]
  We propose a novel convolutional operator for the task of point cloud completion. The proposed operator does not require any max-pooling or voxelization operation. We show that our approach achieves state-of-the-art performance in shape completion at low and high resolutions.
  arXiv  Detail & Related papers  (2022-05-08T15:31:36Z)
• Attention guided global enhancement and local refinement network for semantic segmentation [5.881350024099048]
  A lightweight semantic segmentation network is developed using the encoder-decoder architecture. A Global Enhancement Method is proposed to aggregate global information from high-level feature maps. A Local Refinement Module is developed by utilizing the decoder features as the semantic guidance. The two methods are integrated into a Context Fusion Block, and based on that, a novel Attention guided Global enhancement and Local refinement Network (AGLN) is elaborately designed.
  arXiv  Detail & Related papers  (2022-04-09T02:32:24Z)
• Small Lesion Segmentation in Brain MRIs with Subpixel Embedding [105.1223735549524]
  We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network.
  arXiv  Detail & Related papers  (2021-09-18T00:21:17Z)
• UNETR: Transformers for 3D Medical Image Segmentation [8.59571749685388]
  We introduce a novel architecture, dubbed as UNEt TRansformers (UNETR), that utilizes a pure transformer as the encoder to learn sequence representations of the input volume. We have extensively validated the performance of our proposed model across different imaging modalities.
  arXiv  Detail & Related papers  (2021-03-18T20:17:15Z)
• Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers [149.78470371525754]
  We treat semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR) SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes.
  arXiv  Detail & Related papers  (2020-12-31T18:55:57Z)
• A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection [74.88284082187462]
  One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps. We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
  arXiv  Detail & Related papers  (2020-12-18T10:51:49Z)
• On the Sub-Layer Functionalities of Transformer Decoder [74.83087937309266]
  We study how Transformer-based decoders leverage information from the source and target languages. Based on these insights, we demonstrate that the residual feed-forward module in each Transformer decoder layer can be dropped with minimal loss of performance.
  arXiv  Detail & Related papers  (2020-10-06T11:50:54Z)
• Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation [56.44853893149365]
  Single encoder-decoder methodologies for semantic segmentation are reaching their peak in terms of segmentation quality and efficiency per number of layers. We propose a new architecture based on a decoder which uses a set of shallow networks for capturing more information content. In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects.
  arXiv  Detail & Related papers  (2020-07-19T18:44:34Z)

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.