FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background

• URL: http://arxiv.org/abs/2407.09379v1
• Date: Fri, 12 Jul 2024 15:57:52 GMT
• Title: FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background
• Authors: Muhammad Ali, Mamoona Javaid, Mubashir Noman, Mustansar Fiaz, Salman Khan,
• Abstract summary: Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios. We propose a feature amplification network (FANet) as a backbone network that incorporates semantic information using a novel feature enhancement module at multi-stages. Our experimental results demonstrate the state-of-the-art performance compared to existing methods.
• Score: 9.970265640589966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios including cluttered backgrounds and translucent objects, etc. To handle these challenges, we propose a feature amplification network (FANet) as a backbone network that incorporates semantic information using a novel feature enhancement module at multi-stages. To achieve this, we propose an adaptive feature enhancement (AFE) block that benefits from both a spatial context module (SCM) and a feature refinement module (FRM) in a parallel fashion. SCM aims to exploit larger kernel leverages for the increased receptive field to handle scale variations in the scene. Whereas our novel FRM is responsible for generating semantic cues that can capture both low-frequency and high-frequency regions for better segmentation tasks. We perform experiments over challenging real-world ZeroWaste-f dataset which contains background-cluttered and translucent objects. Our experimental results demonstrate the state-of-the-art performance compared to existing methods.

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)
• MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation [2.0293118701268154]
  We propose a novel MultiResolution Feature Perturbation (MRFP) technique to randomize domain-specific fine-grained features and perturb style of coarse features. MRFP helps state-of-the-art deep neural networks to learn robust domain invariant features for simulation-to-real semantic segmentation.
  arXiv  Detail & Related papers  (2023-11-30T08:02:49Z)
• MFPNet: Multi-scale Feature Propagation Network For Lightweight Semantic Segmentation [5.58363644107113]
  We propose a novel lightweight segmentation architecture, called Multi-scale Feature Propagation Network (Net) We design a robust-Decoder structure featuring symmetrical residual blocks that consist of flexible bottleneck residual modules (BRMs) Taking benefit of their capacity to model latent long-range contextual relationships, we leverage Graph Convolutional Networks (GCNs) to facilitate multiscale feature propagation between the BRM blocks.
  arXiv  Detail & Related papers  (2023-09-10T02:02:29Z)
• RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation [44.45402982171703]
  We introduce a Reciprocal Feature Evolution Network (RFENet) for effective glass-like object segmentation. RFENet achieves state-of-the-art performance on three popular public datasets.
  arXiv  Detail & Related papers  (2023-07-12T11:45:22Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth [83.94528876742096]
  We tackle the MTL problem of two dense tasks, ie, semantic segmentation and depth estimation, and present a novel attention module called Cross-Channel Attention Module (CCAM) In a true symbiotic spirit, we then formulate a novel data augmentation for the semantic segmentation task using predicted depth called AffineMix, and a simple depth augmentation using predicted semantics called ColorAug. Finally, we validate the performance gain of the proposed method on the Cityscapes dataset, which helps us achieve state-of-the-art results for a semi-supervised joint model based on depth and semantic
  arXiv  Detail & Related papers  (2022-06-21T17:40:55Z)
• Support-set based Multi-modal Representation Enhancement for Video Captioning [121.70886789958799]
  We propose a Support-set based Multi-modal Representation Enhancement (SMRE) model to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal interactions in a self-supervised way.
  arXiv  Detail & Related papers  (2022-05-19T03:40:29Z)
• Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images [54.08240004593062]
  We propose an end-to-end multi-category instance segmentation model, which consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB) SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map. SCMB extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales.
  arXiv  Detail & Related papers  (2021-07-25T08:53:59Z)
• CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation [18.63596070055678]
  We propose a light Cascaded Selective Resolution Network (CSRNet) to improve the performance of real-time segmentation. The proposed network builds a three-stage segmentation system, which integrates feature information from low resolution to high resolution. Experiments on two well-known datasets demonstrate that the proposed CSRNet effectively improves the performance for real-time segmentation.
  arXiv  Detail & Related papers  (2021-06-08T14:22:09Z)
• Deep feature selection-and-fusion for RGB-D semantic segmentation [8.831857715361624]
  This work proposes a unified and efficient feature selectionand-fusion network (FSFNet) FSFNet contains a symmetric cross-modality residual fusion module used for explicit fusion of multi-modality information. Compared with the state-of-the-art methods, experimental evaluations demonstrate that the proposed model achieves competitive performance on two public datasets.
  arXiv  Detail & Related papers  (2021-05-10T04:02:32Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)

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.