[Paper Review] Language models are few-shot learners
Language models are few-shot learners Brown, Tom, et al. “Language models are few-shot learners.” Advances in neural information processing systems 33 (20...
Posts by Category
- nlp paper 13
- Video Super-Resolution paper 6
- Face detection paper 5
- person re-identification paper 4
- Object detection paper 3
- Deep learning paper 3
- Semantic Segmentation paper 3
- Face recognition paper 2
- Video Inpainting paper 2
- Text detection paper 2
- Text recognition paper 2
- Dialogue State Tracking paper 2
- Video Deblurring paper 1
- Space-Time Video Super-Resolution paper 1
- Video decaptioning paper 1
- Multi-Object Tracking paper 1
- Speech Recognition paper 1
nlp paper
[Paper Review] Language models are unsupervised multitask learners
Language models are unsupervised multitask learners Radford, Alec, et al. “Language models are unsupervised multitask learners.” OpenAI blog 1.8 (2019): 9...
[Paper Review] Exploring the limits of transfer learning with a unified text-to-text transformer
Exploring the limits of transfer learning with a unified text-to-text transformer Raffel, Colin, et al. “Exploring the limits of transfer learning with a ...
[Paper Review] Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension Lewis, Mike, et al. “Bart: Denoising seq...
[Paper Review] Deep contextualized word representations
Deep contextualized word representations Matthew E. Peters, Mark Neumann, et al. “Deep contextualized word representations” NAACL 2018.
[Paper Review] Efficient dialogue state tracking by selectively overwriting memory
Efficient dialogue state tracking by selectively overwriting memory Kim, Sungdong, et al. “Efficient dialogue state tracking by selectively overwriting me...
[Paper Review] Transferable multi-domain state generator for task-oriented dialogue systems
Transferable multi-domain state generator for task-oriented dialogue systems Wu, Chien-Sheng, et al. “Transferable multi-domain state generator for task-o...
[Paper Review] Dense passage retrieval for open-domain question answering
Dense passage retrieval for open-domain question answering Karpukhin, Vladimir, et al. “Dense passage retrieval for open-domain question answering.” arXiv...
[Paper Review] Bert: Pre-training of deep bidirectional transformers for language understanding
Bert: Pre-training of deep bidirectional transformers for language understanding Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transform...
[Paper Review] Improving language understanding by generative pre-training
Improving language understanding by generative pre-training Radford, Alec, et al. “Improving language understanding by generative pre-training.” (2018).
[Paper Review] Attention is all you need
Enriching word vectors with subword information Vaswani, Ashish, et al. “Attention is all you need.” Advances in neural information processing systems 30 ...
[Paper Review] Enriching word vectors with subword information
Enriching word vectors with subword information Bojanowski, Piotr, et al. “Enriching word vectors with subword information.” Transactions of the associati...
[Paper Review] Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space Mikolov, Tomas, et al. “Efficient estimation of word representations in vector space.” arXiv ...
Video Super-Resolution paper
[Paper Review] Two-stream action recognition-oriented video super-resolution
Two-stream action recognition-oriented video super-resolution Zhang, Haochen, Dong Liu, and Zhiwei Xiong. “Two-stream action recognition-oriented video su...
[Paper Review] TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution
TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution Tian, Yapeng, et al. “TDAN: Temporally-Deformable Alignment Network for Video Sup...
[Paper Review] Fast spatio-temporal residual network for video super-resolution
Fast spatio-temporal residual network for video super-resolution Li, Sheng, et al. “Fast spatio-temporal residual network for video super-resolution.” Pro...
[Paper Review] Edvr: Video restoration with enhanced deformable convolutional networks
Edvr: Video restoration with enhanced deformable convolutional networks Wang, Xintao, et al. “Edvr: Video restoration with enhanced deformable convolution...
[Paper Review] Recurrent back-projection network for video super-resolution
Recurrent back-projection network for video super-resolution Haris, Muhammad, Gregory Shakhnarovich, and Norimichi Ukita. “Recurrent back-projection netwo...
[Paper Review] Frame-recurrent video super-resolution
Frame-recurrent video super-resolution Sajjadi, Mehdi SM, Raviteja Vemulapalli, and Matthew Brown. “Frame-recurrent video super-resolution.” Proceedings o...
Face detection paper
[Paper Review] Selective Refinement Network for High Performance Face Detection
Selective Refinement Network for High Performance Face Detection Chi, Cheng, et al. “Selective refinement network for high performance face detection.” Pr...
[Paper Review] DSFD: dual shot face detector
DSFD: dual shot face detector Li, Jian, et al. “DSFD: dual shot face detector.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit...
[Paper Review] Pyramidbox: A context-assisted single shot face detector
Pyramidbox: A context-assisted single shot face detector Tang, Xu, et al. “Pyramidbox: A context-assisted single shot face detector.” Proceedings of the E...
[Paper Review] RetinaFace : Single-stage Dense Face Localisation in the Wild
RetinaFace : Single-stage Dense Face Localisation in the Wild Deng, Jiankang, et al. “Retinaface: Single-stage dense face localisation in the wild.” arXiv...
[Paper Review] WIDER FACE : A Face Detection Benchmark
WIDER FACE : A Face Detection Benchmark Yang, Shuo, et al. “Wider face: A face detection benchmark.” Proceedings of the IEEE conference on computer vision...
person re-identification paper
[Paper Review] Abd-net: Attentive but diverse person re-identification
Abd-net: Attentive but diverse person re-identification Chen, Tianlong, et al. “Abd-net: Attentive but diverse person re-identification.” Proceedings of t...
[Paper Review] FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
[Paper Review] Deep learning for person re-identification: A survey and outlook
Deep learning for person re-identification: A survey and outlook. Ye, Mang, et al. “Deep learning for person re-identification: A survey and outlook.” arX...
[Paper Review] A strong baseline and batch normalization neck for deep person re-identification
A strong baseline and batch normalization neck for deep person re-identification Luo, Hao, et al. “A strong baseline and batch normalization neck for deep...
Object detection paper
[Paper Review] Mask r-cnn
Mask r-cnn He, Kaiming, et al. “Mask r-cnn.” Proceedings of the IEEE international conference on computer vision. 2017.
[Paper Review] Fast r-cnn
Fast r-cnn Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE international conference on computer vision. 2015.
[Paper Review] You only look once: Unified, real-time object detection
You only look once: Unified, real-time object detection Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of t...
Deep learning paper
[Paper Review] Spatial transformer networks
Spatial transformer networks Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. “Spatial transformer networks.” Advances in neural information processi...
[Paper Review] Learning spatiotemporal features with 3d convolutional networks
Learning spatiotemporal features with 3d convolutional networks Tran, Du, et al. “Learning spatiotemporal features with 3d convolutional networks.” Procee...
[Paper Review] Deformable convolutional networks
Deformable convolutional networks Dai, Jifeng, et al. “Deformable convolutional networks.” Proceedings of the IEEE international conference on computer vi...
Semantic Segmentation paper
[Paper Review] Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Segnet: A deep convolutional encoder-decoder architecture for image segmentation Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. “Segnet: A deep...
[Paper Review] Fully convolutional networks for semantic segmentation
Fully convolutional networks for semantic segmentation Long, Jonathan, Evan Shelhamer, and Trevor Darrell. “Fully convolutional networks for semantic segm...
[Paper Review] Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs Chen, Liang-Chieh, et al. “Deeplab: Semant...
Face recognition paper
[Paper Review] Support Vector Guided Softmax Loss for Face Recognition paper
Support Vector Guided Softmax Loss for Face Recognition Wang, Xiaobo, et al. “Support vector guided softmax loss for face recognition.” arXiv preprint arX...
[Paper Review] Focal Loss for Dense Object Detection paper
Focal Loss for Dense Object Detection Lin, Tsung-Yi, et al. “Focal loss for dense object detection.” Proceedings of the IEEE international conference on c...
Video Inpainting paper
[Paper Review] Video inpainting by jointly learning temporal structure and spatial details
Video inpainting by jointly learning temporal structure and spatial details Wang, Chuan, et al. “Video inpainting by jointly learning temporal structure a...
[Paper Review] Deep video inpainting
Deep video inpainting Kim, Dahun, et al. “Deep video inpainting.” proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
Text detection paper
[Paper Review] Character region awareness for text detection
Character region awareness for text detection Baek, Youngmin, et al. “Character region awareness for text detection.” Proceedings of the IEEE/CVF Conferen...
[Paper Review] Efficient and accurate arbitrary-shaped text detection with pixel aggregation network
Efficient and accurate arbitrary-shaped text detection with pixel aggregation network Wang, Wenhai, et al. “Efficient and accurate arbitrary-shaped text d...
Text recognition paper
[Paper Review] Star-net: a spatial attention residue network for scene text recognition
Star-net: a spatial attention residue network for scene text recognition Liu, Wei, et al. “Star-net: a spatial attention residue network for scene text re...
[Paper Review] PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit Mou, Yongqiang, et al. “PlugNet: Degradation Aware Scene...
Dialogue State Tracking paper
[Paper Review] Efficient dialogue state tracking by selectively overwriting memory
Efficient dialogue state tracking by selectively overwriting memory Kim, Sungdong, et al. “Efficient dialogue state tracking by selectively overwriting me...
[Paper Review] Transferable multi-domain state generator for task-oriented dialogue systems
Transferable multi-domain state generator for task-oriented dialogue systems Wu, Chien-Sheng, et al. “Transferable multi-domain state generator for task-o...
Video Deblurring paper
[Paper Review] Spatio-temporal filter adaptive network for video deblurring
Spatio-temporal filter adaptive network for video deblurring Zhou, Shangchen, et al. “Spatio-temporal filter adaptive network for video deblurring.” Proce...
Space-Time Video Super-Resolution paper
[Paper Review] Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution Xiang, Xiaoyu, et al. “Zooming Slow-Mo: Fast and Accurate One-Stage Space-T...
Video decaptioning paper
[Paper Review] Deep blind video decaptioning by temporal aggregation and recurrence
Deep blind video decaptioning by temporal aggregation and recurrence Kim, Dahun, et al. “Deep blind video decaptioning by temporal aggregation and recurre...
Multi-Object Tracking paper
[Paper Review] FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Speech Recognition paper
[Paper Review] Contextnet: Improving convolutional neural networks for automatic speech recognition with global context
Contextnet: Improving convolutional neural networks for automatic speech recognition with global context Han, Wei, et al. “Contextnet: Improving convoluti...