Dynamic-Structured Semantic Propagation Network

Liang, Xiaodan; Xing, Eric P.; Zhou, Hongfei

doi:10.1109/cvpr.2018.00085

Cited by 141 publications

(70 citation statements)

References 36 publications

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“…Backbone mIoU (%) RefineNet [18] ResNet-152 40.70 UperNet [35] ResNet-101 42.65 DSSPN [17] ResNet-101 43.68 PSANet [48] ResNet-101 43.77 SAC [45] ResNet-101 44.30 EncNet [43] ResNet-101 44.65 PSPNet [47] ResNet-101 43.29 PSPNet [47] ResNet-269 44.94 Ours…”

Section: Methodsmentioning

confidence: 99%

Asymmetric Non-Local Neural Networks for Semantic Segmentation

Zhu

Mengdu

Bai

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

671

362

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The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the nonlocal block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance. Extensive experiments on semantic segmentation benchmarks demonstrate the effectiveness and efficiency of our work. In particular, we report the state-of-the-art performance of 81.3 mIoU on the Cityscapes test set. For a 256 × 128 input, APNB is around 6 times faster than a non-local block on GPU while 28 times smaller in GPU running memory occupation. Code is available at: https://github.com/ MendelXu/ANN.git. * Equal contribution † Corresponding author < l a t e x i t s h a 1 _ b a s e 6 4 = " h Z k z g Y O / g V + m e P P y R w 8 d X I Q M H C U = " > A A A B 6 3 i c b Z D L S g M x F I Z P 6 q 3 W W 9 W l m 2 A R X E i Z c a P L g h u X F e w F 2 q F k 0 k w n N M k M S U Y o Q 1 / B j Q t F 3 P p C 7 n w b M + 0 s t P W H

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Section: Methodsmentioning

confidence: 99%

Asymmetric Non-Local Neural Networks for Semantic Segmentation

Zhu

Mengdu

Bai

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

671

362

View full text Add to dashboard Cite

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“…Deeplab-v2 [9] 70.4 RefineNet-Res101 [43] 73.6 DSSPN-Universal [41] 76.6 GCN [56] 76.9 DepthSet [35] 78.2 PSPNet [75] 78.4 AAF [34] 79.1 DFN [72] 79.3 PSANet [76] 80.1 DenseASPP-DenseNet161 [71] 80.6…”

Section: Methods Mean Ioumentioning

confidence: 99%

Semantic Correlation Promoted Shape-Variant Context for Segmentation

Ding

Jiang

Shuai

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

173

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Context is essential for semantic segmentation. Due to the diverse shapes of objects and their complex layout in various scene images, the spatial scales and shapes of contexts for different objects have very large variation. It is thus ineffective or inefficient to aggregate various context information from a predefined fixed region. In this work, we propose to generate a scale-and shape-variant semantic mask for each pixel to confine its contextual region. To this end, we first propose a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask. Using the inferred spatial scope of the contextual region, we propose a shape-variant convolution, of which the receptive field is controlled by the shape mask that varies with the appearance of input. In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region. Furthermore, this work also proposes a labeling denoising model to reduce wrong predictions caused by the noisy low-level features. Without bells and whistles, the proposed segmentation network achieves new state-of-the-arts consistently on the six public segmentation datasets.

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“…RefineNet [22] 33.6 Ding et al [7] 35.7 DSSPN [20] 38.9 SGR [19] 39.1 DANet [16] 39.7 ACNet 40.1 Table 8. Segmentation results on COCO Stuff testing set.…”

Section: Backbone Methods Miou(%)mentioning

confidence: 99%

Adaptive Context Network for Scene Parsing

Liu

Wang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

141

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Recent works attempt to improve scene parsing performance by exploring different levels of contexts, and typically train a well-designed convolutional network to exploit useful contexts across all pixels equally. However, in this paper, we find that the context demands are varying from different pixels or regions in each image. Based on this observation, we propose an Adaptive Context Network (AC-Net) to capture the pixel-aware contexts by a competitive fusion of global context and local context according to different per-pixel demands. Specifically, when given a pixel, the global context demand is measured by the similarity between the global feature and its local feature, whose reverse value can be used to measure the local context demand. We model the two demand measurements by the proposed global context module and local context module, respectively, to generate adaptive contextual features. Furthermore, we import multiple such modules to build several adaptive context blocks in different levels of network to obtain a coarse-to-fine result.

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Dynamic-Structured Semantic Propagation Network

Cited by 141 publications

References 36 publications

Asymmetric Non-Local Neural Networks for Semantic Segmentation

Asymmetric Non-Local Neural Networks for Semantic Segmentation

Semantic Correlation Promoted Shape-Variant Context for Segmentation

Adaptive Context Network for Scene Parsing

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