| 1 |
Cimpoi, Mircea, Subhransu Maji, and Andrea Vedaldi. "Deep filter banks for texture recognition and segmentation." CVPR. 2015. |
412 |
Designed a new texture descriptor, FV-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. |
| 2 |
Zhang, Hang, Jia Xue, and Kristin Dana. "Deep ten: Texture encoding network." CVPR. 2017. |
48 |
Propose an Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. |
| 3 |
Zhang, Hang, et al. "Context encoding for semantic segmentation." CVPR. 2018. |
134 |
Explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. |
| 4 |
Wang, Yaming, Vlad I. Morariu, and Larry S. Davis. "Learning a discriminative filter bank within a CNN for fine-grained recognition." CVPR. 2018. |
16 |
The work show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. |
| 5 |
Zhao, Hengshuang, et al. "Pyramid scene parsing network." CVPR. 2017. |
1482 |
This paper exploit the capability of global context information by different-regionbased context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) |
| 6 |
Yang, Xiao, et al. "Learning to extract semantic structure from documents using multimodal fully convolutional neural networks." CVPR. 2017. |
21 |
The paper present an end-to-end, multimodal (image and text), fully convolutional network for extracting semantic structures from document images. |
| 7 |
Das, Arindam, et al. "Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks." ICPR, 2018. |
3 |
The paper proposes a region-based Deep Convolutional Neural Network framework is presented for document structure learning. The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification. |
| 8 |
Xiong, Yuwen, et al. "Upsnet: A unified panoptic segmentation network." CVPR. 2019. |
13 |
On top of a single backbone residual network, design a deformable convolution based semantic segmentation head and a Mask R-CNN style instance segmentation head which solve these two subtasks simultaneously and achive accurate segmentation |
| 9 |
Huang, Zhaojin, et al. "Mask scoring r-cnn." CVPR 2019. |
9 |
? |
| 10 |
He, Kaiming, et al. "Mask r-cnn." ICCV. 2017. |
3178 |
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| 11 |
Iwana, Brian Kenji, et al. "Judging a Book by its Cover." arXiv preprint arXiv:1610.09204 (2016). |
9 |
This paper introduce a problem of identify the genres of book from the cover page. and they solve it as a classification problem using deep neural network. |
| 12 |
Christlein, Vincent, and Andreas Maier. "Encoding CNN activations for writer recognition." DAS 2018. |
7 |
This paper compare the established VLAD encoding with triangulation embedding. It further investigate generalized max pooling as an alternative to sum pooling and the impact of decorrelation and Exemplar SVMs in the problem of writter identification. |
| 13 |
Lu, Liqiong, et al. "Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images." IEEE Access 7 (2019): 52669-52679. |
1 |
The paper presents a novel framework integrating Local CNN and Global CNN both of which are based on ResNet-20 for script identification. |