GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection

• URL: http://arxiv.org/abs/2108.11117v1
• Date: Wed, 25 Aug 2021 08:33:49 GMT
• Title: GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection
• Authors: C. Zheng, D. Shi, X. Yan, D. Liang, M. wei, X. Yang, Y. Guo, H. Xie
• Abstract summary: We exploit label decoupling to decompose the labeled ground-truth (GT) map into an interior-diffusion map and a boundary-diffusion map. The GT map in collaboration with the two newly generated maps breaks the imbalanced distribution of the object boundary, leading to improved glass detection quality. We develop an attention-based boundary-aware feature Mosaic module to integrate multi-modal information.
• Score: 1.1825946875790057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from traditional deep learning-based wisdoms that simply use the object boundary as auxiliary supervision, we exploit label decoupling to decompose the original labeled ground-truth (GT) map into an interior-diffusion map and a boundary-diffusion map. The GT map in collaboration with the two newly generated maps breaks the imbalanced distribution of the object boundary, leading to improved glass detection quality. We have three key contributions to solve the transparent glass detection problem: (1) We propose a three-stream neural network (call GlassNet for short) to fully absorb beneficial features in the three maps. (2) We design a multi-scale interactive dilation module to explore a wider range of contextual information. (3) We develop an attention-based boundary-aware feature Mosaic module to integrate multi-modal information. Extensive experiments on the benchmark dataset exhibit clear improvements of our method over SOTAs, in terms of both the overall glass detection accuracy and boundary clearness.

Related papers

• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• Fourier Boundary Features Network with Wider Catchers for Glass Segmentation [12.465008923418406]
  We propose a new method for constraining the segmentation of reflection surface and penetrating glass. The proposed method yields better segmentation performance compared with the state-of-the-art (SOTA) methods in glass image segmentation.
  arXiv  Detail & Related papers  (2024-05-15T15:52:27Z)
• Glass Segmentation with Multi Scales and Primary Prediction Guiding [2.66512000865131]
  Glass-like objects can be seen everywhere in our daily life which are hard for existing methods to segment them. We propose MGNet, which consists of a FineRescaling and Merging module (FRM) to improve the ability to extract semantics. We supervise the model with a novel loss function with the uncertainty-aware loss to produce high-confidence segmentation maps.
  arXiv  Detail & Related papers  (2024-02-13T16:14:32Z)
• A bioinspired three-stage model for camouflaged object detection [8.11866601771984]
  We propose a three-stage model that enables coarse-to-fine segmentation in a single iteration. Our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features. Our network surpasses state-of-the-art CNN-based counterparts without unnecessary complexities.
  arXiv  Detail & Related papers  (2023-05-22T02:01:48Z)
• Unleash the Potential of Image Branch for Cross-modal 3D Object Detection [67.94357336206136]
  We present a new cross-modal 3D object detector, namely UPIDet, which aims to unleash the potential of the image branch from two aspects. First, UPIDet introduces a new 2D auxiliary task called normalized local coordinate map estimation. Second, we discover that the representational capability of the point cloud backbone can be enhanced through the gradients backpropagated from the training objectives of the image branch.
  arXiv  Detail & Related papers  (2023-01-22T08:26:58Z)
• Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network [70.53093934205057]
  3D object detection task from lidar or camera sensors is essential for autonomous driving. We propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models.
  arXiv  Detail & Related papers  (2022-07-12T12:35:34Z)
• ORA3D: Overlap Region Aware Multi-view 3D Object Detection [11.58746596768273]
  Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly. We propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adrial Overlap Region Discriversaminator. Our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet.
  arXiv  Detail & Related papers  (2022-07-02T15:28:44Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)
• Enhanced Boundary Learning for Glass-like Object Segmentation [55.45473926510806]
  This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module for generating finer boundary cues. An edge-aware point-based graph convolution network module is proposed to model the global shape representation along the boundary.
  arXiv  Detail & Related papers  (2021-03-29T16:18:57Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)

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.