Deep Dual Learning for Semantic Image Segmentation

By Ping Luo, Guangrun Wang, Liang Lin, Xiaogang Wang. 2017

96 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Achieving Human Parity on Automatic Chinese to English News Translation (2018, background): “Ever since dual learning was proposed, it has been successfully applied to various real-world problems such as question answering [35], image classification [47], image segmentation [25], image to image translation [50, 52, 24], face attribute manipulation [31], and machine translation [19, 43, 23, ”
Context Encoding for Semantic Segmentation (2018, neutral): “Note: the entries using extra than COCO data are not included [6, 39, 50].”
MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features (2017, methodology): “In particular, models based on Fully Convolutional Networks (FCNs) [64, 53] achieve remarkable results in object detection (localize instances) [22, 69, 25, 62, 51, 60, 19, 47] and semantic segmentation (identify semantic class of each pixel) [10, 46, 56, 52, 80, 73, 79, 54].”
Image Segmentation Using Deep Learning: A Survey (2020, background): “Fortunately, with the increasing popularity of deep learning models, the trend has been TABLE 1 Accuracies of Segmentation Models on the PASCALVOC Test Set Method Backbone mIoU FCN [30] VGG-16 62.2 CRF-RNN [38] - 72.0 CRF-RNN [38] - 74.7 BoxSup* [119] - 75.1 Piecewise [39] - 78.0 DPN [40] - 77.5 Dee”
Survey on semantic segmentation using deep learning techniques (2019, background): “Luo et al. [125] present a weakly and semi-supervised dual image segmentation (DIS) learning strategy, which performs segmentation (capturing the accurate object classes), and reconstruction (accurate object shapes and boundaries).”
Reconstruction Network for Video Captioning (2018, background): “Actually, the backward ﬂow can be yielded by the dual learning mechanism that has been introduced into neural machine translation (NMT) [37, 11] and image segmentation [20].”
Expectation-Maximization Attention Networks for Semantic Segmentation (2019, unknown): “Wide ResNet [32] WideResNet-38 84.9 PSPNet [37] ResNet-101 85.4 DeeplabV3 [4] ResNet-101 85.7 PSANet [38] ResNet-101 85.7 EncNet [35] ResNet-101 85.9 DFN [34] ResNet-101 86.2 Exfuse [36] ResNet-101 86.2 IDW-CNN [30] ResNet-101 86.3 SDN [12] DenseNet-161 86.6 DIS [23] ResNet-101 86.8”
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation (2019, methodology): “PSPNet [36] 85.4 DeepLabv3 [4] 85.7 EncNet [34] 85.9 DFN [32] 86.2 IDW-CNN [27] 86.3 CASIA IVA SDN [9] 86.6 DIS [22] 86.8 DeepLabv3+ [5] ing [5], Xception-71 is used as our backbone and the number of iterations is increased to 90 k with a initial learning rate being 0.01.”
Squeeze-and-Attention Networks for Semantic Segmentation (2019, unknown): “As shown in [6, 27, 34, 8].”
Multi-Agent Dual Learning (2019, background): “…and applied to many applications including neural machine translation (NMT) (He et al., 2016; Xia et al., 2017a;b), image-to-image translation (Zhu et al., 2017; Yi et al., 2017; Luo et al., 2017), question answering (Duan et al., 2017; Sun et al., 2019) and image captioning (Huang et al., 2018).”
Small Sample Learning in Big Data Era (2018, background): “…recent years, the dual learning framework is successfully applied to visual question answering(VQA) (Li et al., 2018c), semantic image segmentation (Luo et al., 2017a), image-to-image translation (Zhu et al., 2017b; Yi et al., 2017), zero-shot visual recognition (Chen et al., 2018b), and neural…”