EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation

• URL: http://arxiv.org/abs/2103.08914v1
• Date: Tue, 16 Mar 2021 08:46:57 GMT
• Title: EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation
• Authors: Qihang Yang and Tao Chen and Jiayuan Fan and Ye Lu and Chongyan Zuo and Qinghua Chi
• Abstract summary: Experimental results on the Cityscapes dataset demonstrate that our proposed EADNet achieves segmentation mIoU of 67.1 with smallest number of parameters (only 0.35M) among mainstream lightweight semantic segmentation networks.
• Score: 8.449677920206817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network, to simultaneously reduce computational cost and increase inference speed. In this paper, we propose an efficient asymmetric dilated semantic segmentation network, named EADNet, which consists of multiple developed asymmetric convolution branches with different dilation rates to capture the variable shapes and scales information of an image. Specially, a multi-scale multi-shape receptive field convolution (MMRFC) block with only a few parameters is designed to capture such information. Experimental results on the Cityscapes dataset demonstrate that our proposed EADNet achieves segmentation mIoU of 67.1 with smallest number of parameters (only 0.35M) among mainstream lightweight semantic segmentation networks.

Related papers

• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• 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)
• Lightweight and Progressively-Scalable Networks for Semantic Segmentation [100.63114424262234]
  Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. In this paper, we thoroughly analyze the design of convolutional blocks and the ways of interactions across multiple scales. We devise Lightweight and Progressively-Scalable Networks (LPS-Net) that novelly expands the network complexity in a greedy manner.
  arXiv  Detail & Related papers  (2022-07-27T16:00:28Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• PocketNet: A Smaller Neural Network for 3D Medical Image Segmentation [0.0]
  We derive a new CNN architecture called PocketNet that achieves comparable segmentation results to conventional CNNs while using less than 3% of the number of parameters. We show that PocketNet achieves comparable segmentation results to conventional CNNs while using less than 3% of the number of parameters.
  arXiv  Detail & Related papers  (2021-04-21T20:10:30Z)
• A De-raining semantic segmentation network for real-time foreground segmentation [0.0]
  This paper proposes a lightweight network for the segmentation in rainy environments, named Deraining Semantic Accuracy Network (DRSNet) By analyzing the characteristics of raindrops, the MultiScaleSE Block is targetedly designed to encode the input image. In order to combine semantic information between different encoder and decoder layers, it is proposed to use Asymmetric Skip.
  arXiv  Detail & Related papers  (2021-04-16T04:09:13Z)
• MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation [27.232578592161673]
  We devise a novel lightweight network using a multi-scale context fusion scheme (MSCFNet) The proposed MSCFNet contains only 1.15M parameters, achieves 71.9% Mean IoU and can run at over 50 FPS on a single Titan XP GPU configuration.
  arXiv  Detail & Related papers  (2021-03-24T08:28:26Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)
• EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion [5.815742965809424]
  We propose an Efficient Panoptic Network (EPSNet) to tackle the panoptic segmentation tasks with fast inference speed. Basically, EPSNet generates masks based on simple linear combination of prototype masks and mask coefficients. To enhance the quality of shared prototypes, we adopt a module called "cross-layer attention fusion module"
  arXiv  Detail & Related papers  (2020-03-23T09:11:44Z)
• Gated Path Selection Network for Semantic Segmentation [72.44994579325822]
  We develop a novel network named Gated Path Selection Network (GPSNet), which aims to learn adaptive receptive fields. In GPSNet, we first design a two-dimensional multi-scale network - SuperNet, which densely incorporates features from growing receptive fields. To dynamically select desirable semantic context, a gate prediction module is further introduced.
  arXiv  Detail & Related papers  (2020-01-19T12:32:17Z)

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.