Paper Review
1.[paper-review] Sequence-to-Sequence Domain Adaptation Network for Robust Text Image Recognition
Zhang, Yaping, et al. "Sequence-to-sequence domain adaptation network for robust text image recognition." ...
2.[paper-review] Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Kobayashi, Sosuke. "Contextual augmentation: Data augmentation by words with paradigmatic relations." arXiv preprint arXiv:1805.06201 (2018).
3.[paper-review] Generative adversarial networks with mixture of t-distributions noise for diverse image generation
Sun, Jinxuan, et al. "Generative adversarial networks with mixture of t-distributions noise for diverse image generation." Neural Networks 122 (2020)
4.[paper-review] Unsupervised Data Augmentation for Consistency Training
Xie, Qizhe, et al. "Unsupervised data augmentation for consistency training." arXiv preprint arXiv:1904.12848 (2019).
5.[paper-review] Generative Adversarial Nets
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
6.[paper-review] Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. "Neural machine translation by jointly learning to align and translate."
7.[paper-review] You Only Look Once: Unified, Real-Time Object Detection
Redmon, Joseph, et al. "You only look once: Unified, real-time object detection."
8.[paper-review] Ultradense Word Embeddings by Orthogonal Transformation
Rothe, Sascha, Sebastian Ebert, and Hinrich Schütze. "Ultradense word embeddings by orthogonal transformation."
9.[paper-review] Attention Is All You Need
Vaswani, Ashish, et al. "Attention is all you need." arXiv preprint arXiv:1706.03762 (2017).
10.[paper-review] SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
Bai, Yancheng, et al. "Sod-mtgan: Small object detection via multi-task generative adversarial network."
11.[paper-review] Self-Attention Generative Adversarial Networks
Zhang, Han, et al. "Self-attention generative adversarial networks."
12.[paper-review] Bottom-up Object Detection by Grouping Extreme and Center Points
Zhou, Xingyi, Jiacheng Zhuo, and Philipp Krahenbuhl. "Bottom-up object detection by grouping extreme and center points."
13.[paper-review] CornerNet: Detecting Objects as Paired Keypoints
Law, H., & Deng, J. (2018). Cornernet: Detecting objects as paired keypoints.
14.[paper-review] Objects as Points
Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points.
15.[paper-review] Page Segmentation using a Convolutional Neural Network with Trainable Co-occurrence Features
Lee, J., Hayashi, H., Ohyama, W., & Uchida, S. (2019). Page segmentation using a convolutional neural network with trainable co-occurrence features
16.[paper-review] Non-local Neural Networks
Wang, X., Girshick, R., Gupta, A., & He, K. (2018). Non-local neural networks.
17.[paper-review] Asymmetric Non-local Neural Networks for Semantic Segmentation
Zhu, Z., Xu, M., Bai, S., Huang, T., & Bai, X. (2019). Asymmetric non-local neural networks for semantic segmentation.
18.[paper-review] GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019). Gcnet: Non-local networks meet squeeze-excitation networks and beyond.
19.[paper-review] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An image is worth 16x16 words: Transformers for image recognition at scale
20.[paper-review] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., ... & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows.
21.[paper-review] Proxy Anchor Loss for Deep Metric Learning
Kim, S., Kim, D., Cho, M., & Kwak, S. (2020). Proxy anchor loss for deep metric learning.
22.[paper-review] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks.
23.[paper-review] Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting
Liao, M., Pang, G., Huang, J., Hassner, T., & Bai, X. (2020). Mask textspotter v3: Segmentation proposal network for robust scene text spotting.