S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation

• URL: http://arxiv.org/abs/2206.07298v3
• Date: Sun, 19 May 2024 00:31:40 GMT
• Title: S$^2$-FPN: Scale-ware Strip Attention Guided Feature Pyramid Network for Real-time Semantic Segmentation
• Authors: Mohammed A. M. Elhassan, Chenhui Yang, Chenxi Huang, Tewodros Legesse Munea, Xin Hong, Abuzar B. M. Adam, Amina Benabid,
• Abstract summary: This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation. Specifically, we proposed a lightweight model named Scale-aware Strip Attention Guided Feature Pyramid Network (S$2$-FPN) Our network consists of three main modules: Attention Pyramid Fusion (APF) module, Scale-aware Strip Attention Module (SSAM), and Global Feature Upsample (GFU) module.
• Score: 6.744210626403423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern high-performance semantic segmentation methods employ a heavy backbone and dilated convolution to extract the relevant feature. Although extracting features with both contextual and semantic information is critical for the segmentation tasks, it brings a memory footprint and high computation cost for real-time applications. This paper presents a new model to achieve a trade-off between accuracy/speed for real-time road scene semantic segmentation. Specifically, we proposed a lightweight model named Scale-aware Strip Attention Guided Feature Pyramid Network (S$^2$-FPN). Our network consists of three main modules: Attention Pyramid Fusion (APF) module, Scale-aware Strip Attention Module (SSAM), and Global Feature Upsample (GFU) module. APF adopts an attention mechanisms to learn discriminative multi-scale features and help close the semantic gap between different levels. APF uses the scale-aware attention to encode global context with vertical stripping operation and models the long-range dependencies, which helps relate pixels with similar semantic label. In addition, APF employs channel-wise reweighting block (CRB) to emphasize the channel features. Finally, the decoder of S$^2$-FPN then adopts GFU, which is used to fuse features from APF and the encoder. Extensive experiments have been conducted on two challenging semantic segmentation benchmarks, which demonstrate that our approach achieves better accuracy/speed trade-off with different model settings. The proposed models have achieved a results of 76.2\%mIoU/87.3FPS, 77.4\%mIoU/67FPS, and 77.8\%mIoU/30.5FPS on Cityscapes dataset, and 69.6\%mIoU,71.0\% mIoU, and 74.2\% mIoU on Camvid dataset. The code for this work will be made available at \url{https://github.com/mohamedac29/S2-FPN

Related papers

• PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery [30.522327480291295]
  We propose a novel Mamba-based segmentation network, namely PyramidMamba. Specifically, we design a dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in multi-scale feature fusion. Our PyramidMamba yields state-of-the-art performance on three publicly available datasets.
  arXiv  Detail & Related papers  (2024-06-16T07:43:40Z)
• P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image Segmentation [8.46409964236009]
  Diffusion models and multi-scale features are essential components in semantic segmentation tasks. We propose a new model for semantic segmentation known as the diffusion model with parallel multi-scale branches. Our model demonstrates superior performance based on the J1 metric on both the UAVid and Vaihingen Building datasets.
  arXiv  Detail & Related papers  (2024-05-30T19:40:08Z)
• P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation [1.1470070927586018]
  We propose a real-time semantic segmentation architecture named Pyramid Pooling Axial Transformer (P2AT) The proposed P2AT takes a coarse feature from the CNN encoder to produce scale-aware contextual features. We evaluate P2AT variants on three challenging scene-understanding datasets.
  arXiv  Detail & Related papers  (2023-10-23T15:23:31Z)
• Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation [52.11279360934703]
  Current prevailing Video Object (VOS) methods usually perform dense matching between the current and reference frames after extracting features. We propose a unified VOS framework, coined as JointFormer, for joint modeling of the three elements of feature, correspondence, and a compressed memory.
  arXiv  Detail & Related papers  (2023-08-25T17:30:08Z)
• Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation [1.0499611180329804]
  We propose a novel Factorized Pyramidal Learning (FPL) module to aggregate rich contextual information in an efficient manner. We decompose the spatial pyramid into two stages which enables a simple and efficient feature fusion within the module to solve the notorious checkerboard effect. Based on the FPL module and FIR unit, we propose an ultra-lightweight real-time network, called FPLNet, which achieves state-of-the-art accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-02-23T05:34:51Z)
• SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow [88.97790684009979]
  A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. We propose a Flow Alignment Module (FAM) to learn textitSemantic Flow between feature maps of adjacent levels. We also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps.
  arXiv  Detail & Related papers  (2022-07-10T08:25:47Z)
• Improving Video Instance Segmentation via Temporal Pyramid Routing [61.10753640148878]
  Video Instance (VIS) is a new and inherently multi-task problem, which aims to detect, segment and track each instance in a video sequence. We propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames. Our approach is a plug-and-play module and can be easily applied to existing instance segmentation methods.
  arXiv  Detail & Related papers  (2021-07-28T03:57:12Z)
• EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation [62.210091681352914]
  We study multi-sensor fusion for 3D semantic segmentation for many applications, such as autonomous driving and robotics. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) We propose a two-stream network to extract features from the two modalities separately. The extracted features are fused by effective residual-based fusion modules.
  arXiv  Detail & Related papers  (2021-06-21T10:47:26Z)
• A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation [68.10621089649486]
  We propose Attention Aggregation based Feature Pyramid Network (A2-FPN) to improve multi-scale feature learning. A2-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.
  arXiv  Detail & Related papers  (2021-05-07T11:51:08Z)
• AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing [12.409365458889082]
  We propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics. AttaNet consists of two primary modules: Strip Attention Module (SAM) and Attention Fusion Module (AFM)
  arXiv  Detail & Related papers  (2021-03-10T08:38:29Z)
• Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking [79.80401607146987]
  Existing object tracking usually learns a bounding-box based template to match visual targets across frames, which cannot accurately learn a pixel-wise representation. This paper presents a novel segmentation-based tracking architecture, which is equipped with a local-temporal memory network to learn accurate-temporal correspondence.
  arXiv  Detail & Related papers  (2020-09-21T08:12:02Z)

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.