Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation

• URL: http://arxiv.org/abs/2406.12496v1
• Date: Tue, 18 Jun 2024 10:59:10 GMT
• Title: Reparameterizable Dual-Resolution Network for Real-time Semantic Segmentation
• Authors: Guoyu Yang, Yuan Wang, Daming Shi,
• Abstract summary: RDRNet is a Dual-Resolution Network dedicated to real-time semantic segmentation. RDRNet employs a two-branch architecture, utilizing multi-path blocks during training and re parameterizing them into single-path blocks during inference. Experimental results on the Cityscapes, CamVid, and Pascal VOC 2012 datasets demonstrate that RDRNet outperforms existing state-of-the-art models in terms of both performance and speed.
• Score: 15.83905822380148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation plays a key role in applications such as autonomous driving and medical image. Although existing real-time semantic segmentation models achieve a commendable balance between accuracy and speed, their multi-path blocks still affect overall speed. To address this issue, this study proposes a Reparameterizable Dual-Resolution Network (RDRNet) dedicated to real-time semantic segmentation. Specifically, RDRNet employs a two-branch architecture, utilizing multi-path blocks during training and reparameterizing them into single-path blocks during inference, thereby enhancing both accuracy and inference speed simultaneously. Furthermore, we propose the Reparameterizable Pyramid Pooling Module (RPPM) to enhance the feature representation of the pyramid pooling module without increasing its inference time. Experimental results on the Cityscapes, CamVid, and Pascal VOC 2012 datasets demonstrate that RDRNet outperforms existing state-of-the-art models in terms of both performance and speed. The code is available at https://github.com/gyyang23/RDRNet.

Related papers

• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Multi-Level Aggregation and Recursive Alignment Architecture for Efficient Parallel Inference Segmentation Network [18.47001817385548]
  We propose a parallel inference network customized for semantic segmentation tasks. We employ a shallow backbone to ensure real-time speed, and propose three core components to compensate for the reduced model capacity to improve accuracy. Our framework shows a better balance between speed and accuracy than state-of-the-art real-time methods on Cityscapes and CamVid datasets.
  arXiv  Detail & Related papers  (2024-02-03T22:51:17Z)
• RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer [63.25665813125223]
  We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation. It achieves better trade-off between performance and efficiency than CNN-based models. Experiments on mainstream benchmarks demonstrate the effectiveness of our proposed RTFormer.
  arXiv  Detail & Related papers  (2022-10-13T16:03:53Z)
• Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation [49.17930380106643]
  We propose a novel framework to speed up any architecture with skip-connections for real-time vision tasks. Specifically, at the arrival of each frame, we transform the features from the previous frame to reuse them at specific spatial bins. We then perform partial computation of the backbone network on the regions of the current frame that captures temporal differences between the current and previous frame.
  arXiv  Detail & Related papers  (2022-06-20T07:20:02Z)
• Feature Reuse and Fusion for Real-time Semantic segmentation [0.0]
  How to increase the speed while maintaining high resolution is a problem that has been discussed and solved. We hope to design a light-weight network based on previous design experience and reach the level of state-of-the-art real-time semantic segmentation.
  arXiv  Detail & Related papers  (2021-05-27T06:47:02Z)
• 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)
• HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation [95.47168925127089]
  We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. We design a new type of hypernetwork, composed of a nested U-Net for drawing higher level context features.
  arXiv  Detail & Related papers  (2020-12-21T18:58:18Z)
• Dense Dual-Path Network for Real-time Semantic Segmentation [7.8381744043673045]
  We introduce a novel Dual-Path Network (DDPNet) for real-time semantic segmentation under resource constraints. DDPNet achieves 75.3% mIoU with 52.6 FPS for an input of 1024 X 2048 resolution on a single GTX 1080Ti card.
  arXiv  Detail & Related papers  (2020-10-21T06:11:41Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• TAM: Temporal Adaptive Module for Video Recognition [60.83208364110288]
  temporal adaptive module (bf TAM) generates video-specific temporal kernels based on its own feature map. Experiments on Kinetics-400 and Something-Something datasets demonstrate that our TAM outperforms other temporal modeling methods consistently.
  arXiv  Detail & Related papers  (2020-05-14T08:22:45Z)

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.