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On the iterative refinement of densely connected representation levels for semantic segmentation
[article]
2018
arXiv
pre-print
Preserved Fulltext
This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep ...
FC-DRN has a densely connected backbone composed of residual networks. ...
Acknowledgments The authors would like to thank the developers of Pytorch [32] . We acknowledge the support of the following agencies for research funding and computing support: CI- ...
arXiv:1804.11332v1
fatcat:2yxu5l565verzbvckk6ird3dbm
On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep ...
FC-DRN has a densely connected backbone composed of residual networks. ...
We acknowledge the support of the following agencies for research funding and computing support: CI-FAR, Canada Research Chairs, Compute Canada and Calcul Québec, as well as NVIDIA for the generous GPU ...
doi:10.1109/cvprw.2018.00144
dblp:conf/cvpr/CasanovaCDRB18
fatcat:pdtsol3ygjculab7dalweuzpl4
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
[article]
2019
arXiv
pre-print
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Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively ...
Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods ...
We extract multiple levels of representations in CNNs for dense comparison. ...
arXiv:1903.02351v1
fatcat:cgirp7xrhvbx5f3wm674uspxx4
HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud Processing
[article]
2022
arXiv
pre-print
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To illustrate the improved processing capability, we compare previous point based and GNN models for semantic segmentation with our HPGNN, achieving a significant improvement for GNNs (+36.7 mIoU) on the ...
Connections between multiple levels enable a point to learn features in multiple scales, in a few iterations. ...
Acknowledgement: We thank National Research Council of Sri Lanka for providing computational resources through the grant no. 19-080. ...
arXiv:2206.02153v1
fatcat:ijqtzzulivap7kjlnzroajuwci
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Our extensive evaluation on the public benchmarks demonstrates that ZigZagNet surpasses the state-of-the-art accuracy for both semantic segmentation and instance segmentation tasks. ...
levels of the top-down and the bottom-up hierarchies, in a zig-zag fashion. ...
Acknowledgments We thank the anonymous reviewers for their constructive comments. This work was supported in parts by ...
doi:10.1109/cvpr.2019.00767
dblp:conf/cvpr/LinSSJLC019
fatcat:zabnl2gjazet5fztlomvhsdtme
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
[article]
2021
arXiv
pre-print
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Semantic segmentation is a challenging task in the absence of densely labelled data. Only relying on class activation maps (CAM) with image-level labels provides deficient segmentation supervision. ...
auxiliary tasks to improve the primary task of semantic segmentation using only image-level ground-truth labels. ...
., semantic segmentation) only using image-level ground-truth labels, and performs affinity learning across two dense prediction tasks (i.e., saliency detection and semantic segmentation). ...
arXiv:2107.11787v2
fatcat:qjahvwt5s5akdfi4ew5ns4hkx4
A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
[article]
2022
arXiv
pre-print
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However, the current segmentation algorithms mostly rely on the availability of pixel-level annotations, which are often expensive, tedious, and laborious. ...
The rapid development of deep learning has made a great progress in segmentation, one of the fundamental tasks of computer vision. ...
ACKNOWLEDGMENTS This work was supported by NSFC 62176159, Natural Science Foundation of Shanghai 21ZR1432200 and Shanghai Municipal Science and Technology Major Project 2021SHZDZX0102. ...
arXiv:2207.01223v1
fatcat:i7rgpxrfkrdbfm4effjdcjjr24
Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling
[article]
2018
arXiv
pre-print
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Effective integration of local and global contextual information is crucial for semantic segmentation and dense image labeling. ...
G-FRNet achieves state-of-the-art semantic segmentation results on the CamVid and Horse-Cow Parsing datasets and produces results competitive with the best performing approaches that appear in the literature ...
For recognition problems, the loss of spatial precision is not especially problematic. However, dense image labeling problems (e.g. semantic segmentation [4] ) require pixel-level precision. ...
arXiv:1806.11266v1
fatcat:nzfn3zaxvvbe7ajtxuztzb5a2y
Deep Layer Aggregation
[article]
2019
arXiv
pre-print
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Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. ...
Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. ...
Iterative connections join neighboring stages to progressively deepen and spatially refine the representation. ...
arXiv:1707.06484v3
fatcat:kyyrb4e2vfhq5epsxzx4jurcta
Deep Layer Aggregation
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Even with the depth of features in a convolutional network, a layer in isolation is not enough: compounding and aggregating these representations improves inference of what and where. ...
Although skip connections have been incorporated to combine layers, these connections have been "shallow" themselves, and only fuse by simple, one-step operations. ...
Iterative connections join neighboring stages to progressively deepen and spatially refine the representation. ...
doi:10.1109/cvpr.2018.00255
dblp:conf/cvpr/YuWSD18
fatcat:cfc3y3apxrfdhkrruc4ehwr65y
Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network
[article]
2020
arXiv
pre-print
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Many deep learning based methods have been proposed for retinal vessel segmentation, however few of them focus on the connectivity of segmented vessels, which is quite important for a practical computer-aided ...
Besides, a recursive refinement iteratively applies the same network over the previous segmentation results for progressively boosting the performance while increasing no extra network parameters. ...
Besides, we adopt a recursive refinement for further boosting the connectivity of segmented vessels. ...
arXiv:2004.12776v1
fatcat:zu7bjtqhm5b3bbo47t7p24zww4
Learning deep representations for semantic image parsing: a comprehensive overview
2018
Frontiers of Computer Science
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2018 pre-print arXiv:1810.04377v1 fatcat:67xjrrltbzekphdnq23xyrhmze
In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation ...
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the ...
Instance-level semantic segmentation. In contrast to category-level segmentation, it requires precise segmentation of each object and correct detection of all the object instances in one image [2] . ...
doi:10.1007/s11704-018-7195-8
fatcat:p5hvfwhl5rbork5vf4rpnx3h6u
Parallel Fully Convolutional Network for Semantic Segmentation
2020
IEEE Access
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Fully convolutional networks (FCNs) have been widely applied for dense classification tasks such as semantic segmentation. ...
As a large number of works based on FCNs are proposed, various semantic segmentation models have been improved significantly. ...
They are proved effective for feature generation on the task of semantic image segmentation, hence becoming the most prevalent method for semantic segmentation. ...
doi:10.1109/access.2020.3042254
fatcat:2sdkyxjm25ajxezi2tu63aanmi
Filling the Gaps in Atrous Convolution: Semantic Segmentationwith a Better Context
2019
IEEE Access
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The heart of our approach is a high-level feature aggregation module that augments sparsely connected atrous convolution with dense local and layer-wise connections to avoid gridding artifacts. ...
Besides, we employ a novel feature pyramid augmentation and semantic refinement unit to generate low-and mid-level features that are mixed with high-level features at the decoder. ...
Similarly, [10] uses dense FIGURE 1. The importance of local and high-level information for semantic segmentation. ...
doi:10.1109/access.2019.2946031
fatcat:uube3oolcvbgvkz7fellapffpq
Attention guided global enhancement and local refinement network for semantic segmentation
[article]
2022
arXiv
pre-print
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Second, low-level features may bring noises to the network decoder through skip connections for the inadequacy of semantic concepts in early encoder layers. ...
The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. ...
Most of the recent approaches for semantic segmentation are based on Fully Convolutional Network (FCN) [4] . ...
arXiv:2204.04363v1
fatcat:epwtjq7h3fe3ph526jmj57isqe
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