[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...
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[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] 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] 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] Spatial transformer networks
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[Paper Review] Efficient dialogue state tracking by selectively overwriting memory
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[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] 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...
[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
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[Paper Review] Enriching word vectors with subword information
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[Paper Review] Efficient Estimation of Word Representations in Vector Space
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[Paper Review] Abd-net: Attentive but diverse person re-identification
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[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] 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...
[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] 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] PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
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[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] Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
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[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...
[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 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...
[Paper Review] Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
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[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...
[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] 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.
[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] 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] Deformable convolutional networks
Deformable convolutional networks Dai, Jifeng, et al. “Deformable convolutional networks.” Proceedings of the IEEE international conference on computer vi...
[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] 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] 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...
[Paper Review] Pyramidbox: A context-assisted single shot face detector
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[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] 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...
[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...
[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...