> For the complete documentation index, see [llms.txt](https://awesome-davian.gitbook.io/awesome-reviews/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review.md).

# \[2022 Spring] Paper review

- [RobustNet \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-transferability-eng.md): Yizhou et al. / Revisiting the Transferability of Supervised Pretraining an MLP Perspective / CVPR 2022
- [DPT \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-dpt-kor.md): Rene et al. / Vision Transformers for Dense Prediction / ICCV 2021
- [DALL-E \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/icml-2021-dalle-kor.md): Ramech et al. / Zero-shot Text-to-Image Generation / IMCL 2021
- [VRT: A Video Restoration Transformer \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/arxiv-2022-vrt-kor.md): Liang et al. / VRT - A Video Restoration Transformer / Arxiv 2022
- [Barbershop \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/siggraphasia-2021-barbershop-kor.md): Peihao et al. / Barbershop; GAN-based Image Compositing using Segmentation Masks / SIGGRAPH Asia 2021
- [Barbershop \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/siggraphasia-2021-barbershop-eng.md): Peihao et al. / Barbershop; GAN-based Image Compositing using Segmentation Masks / SIGGRAPH Asia 2021
- [REFICS \[ENG\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr2022_refics.md)
- [Deep texture manifold \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2018-deeptexture-kor.md): Xue et al. / Deep Texture Manifold for Ground Terrain Recognition / Venue
- [SlowFast Networks \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2019-slowfastnetworks-kor.md): Christoph et al. / SlowFast Networks for Video Recognition / ICCV 2019
- [SCAN \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/eccv-2020-scan-eng.md): Gansbeke et al. / SCAN Learning to Classify Images without Labels / ECCV 2020
- [Chaining a U-Net With a Residual U-Net for Retinal Blood Vessels Segmentation \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/curu-kor.md): CURU-kor
- [Chaining a U-Net With a Residual U-Net for Retinal Blood Vessels Segmentation \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/curu-eng.md)
- [Patch Cratf : Video Denoising by Deep Modeling and Patch Matching \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-pacnet-eng.md): Vaksman et al. / Patch Craft; Video Denoising by Deep Modeling and Patch Matching / ICCV 2021
- [LAFITE: Towards Language-Free Training for Text-to-Image Generation \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-lafite-kor.md): Yufan Zhou / LAFITE; Towards Language-Free Training for Text-to-Image Generation / CVPR 2022
- [RegSeg \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/arxiv-2021-regseg.md): (Description) Roland Gao / Rethink Dilated Convolution for Real-time Semantic Segmentation / arXiv 2021
- [D-NeRF \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr_2021_d-nerf_-eng.md): Pumarola et al. / D-NeRF; Neural Radiance Fields for Dynamic Scenes / CVPR 2021
- [SimCLR \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/icml-2020-simcrl-kor.md): Ting Chen al. / A Simple Framework for Contrastive Learning of Visual Representation / ICML '2020
- [LabOR \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-labor-kor.md)
- [LabOR \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-labor-eng.md)
- [SegFormer \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/neurips-2021-segformer-kor.md): xie et al. / SegFormer; Simple and Efficient Design for Semantic Segmentation with Transformers / NeurIPS 2021
- [Self-Calibrating Neural Radiance Fields \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-scnerf-kor.md): Jeong et al. / Self-Calibrating Neural Radiance Fields / ICCV 2021
- [Self-Calibrating Neural Radiance Fields \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-scnerf-eng.md): Jeong et al. / Self-Calibrating Neural Radiance Fields / ICCV 2021
- [GIRAFFE \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-giraffe-kor.md): Niemeyer et al. / GIRAFFE] Representing Scenes as Compositional Generative Neural Feature Fields / CVPR 2021 (oral, best paper award)
- [GIRAFFE \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-giraffe-eng.md): Niemeyer et al. / GIRAFFE] Representing Scenes as Compositional Generative Neural Feature Fields / CVPR 2021 (oral, best paper award)
- [DistConv \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/eccv-2018-distconv-kor.md): Keisuke Tateno et al. / Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images / ECCV 2018
- [Nesterov and Scale-Invariant Attack \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2020-sinifgsm-kor.md): Lin et al. / Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks / ICLR 2020
- [OutlierExposure \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2019-outlierexposure-eng.md): Hendrycks et al. / Deep Anomaly Detection using Outlier Exposure / ICLR 2019
- [TSNs \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-tsns-kor.md): Sun, Guolei, et al. / Task Switching Network for Multi-Task Learning / ICCV 2021
- [TSNs \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-tsns-eng.md): Sun, Guolei, et al. / Task Switching Network for Multi-Task Learning / ICCV 2021
- [Improving the Transferability of Adversarial Samples With Adversarial Transformations \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-transferability-kor.md): Wu, Weibin, et al. / Improving the Transferability of Adversarial Samples with Adversarial Transformations / CVPR2021
- [VOS: OOD detection by Virtual Outlier Synthesis \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2022-vos-kor.md): Du et al. / VOS-Learning What You Don’t Know by Virtual Outlier Synthesis / ICLR 2022 Poster
- [MultitaskNeuralProcess \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2022-multitaskneuralprocess-kor.md): (Description) Kim et al. / MULTI-TASK NEURAL PROCESSES / ICRL2022
- [RSLAD \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-rslad-eng.md): Bojia Zi / Revisiting Adversarial Robustness Distillation; Robust Soft Labels Make Student Better / ICCV 2021
- [Deep Learning for 3D Point Cloud Understanding: A Survey \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/deep-learning-for-3d-point-cloud-understanding-eng.md): Haoming Lu et al./ Deep Learning for 3D Point Cloud Understanding; A Survey
- [BEIT \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2022-beit-kor.md): Bao et al. / BEIT - BERT Pre-Training of Image Transformers / ICLR 2022 Oral
- [Divergence-aware Federated Self-Supervised Learning \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2022-fedema-eng.md): Zhuang et al. / Divergence-aware Federated Self-Supervised Learning / ICLR 2022
- [NeRF-W \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/ieee-nerf-in-the-wild-kor.md): (Description) Martin-Brualla et al. / NeRF in the Wild; Neural Radiance Fields for Unconstrained Photo Collections / IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021
- [Learning Multi-Scale Photo Exposure Correction \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-photoexposure-eng.md): Afifi et al. / Learning Multi-Scale Photo Exposure Correction / CVPR 2021
- [ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/eccv-2020-reactnet-eng.md): Zechun Liu et al. / ReActNet; Towards Precise Binary Neural Network with Generalized Activation Functions / ECCV 2020
- [ViT \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2021-vit-eng.md): Dosovitskiy et al. / An Image is Worth 16\*16 Words; Transformers for Image Recognition at Scale / ICLR 2021
- [CrossTransformer \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/neurips-2020-crosstransformer-kor.md): Carl Doersch et al. / CrossTransformer - spatially-aware few-shot transfer / NeurIPS 2020
- [NeRF \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/eccv-2020-nerf-kor.md)
- [RegNeRF \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-regnerf-kor.md): Niemeyer et al. / RegNeRF - Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs / CVPR 2022
- [Image Inpainting with External-internal Learning and Monochromic Bottleneck \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-image-inpainting-eng.md): Tengfei Wang et al. / Image Inpainting with External-internal Learning and Monochromic Bottleneck / CVPR 2021
- [CLIP-NeRF \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-clipnerf-kor.md): Can Wang et al. / CLIP-NeRF; Text-and-Image Driven Manipulation of Neural Radiance Fields / CVPR 2022
- [CLIP-NeRF \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-clipnerf-eng.md): Can Wang et al. / CLIP-NeRF; Text-and-Image Driven Manipulation of Neural Radiance Fields / CVPR 2022
- [DINO: Emerging Properties in Self-Supervised Vision Transformers \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-dino-eng.md): Mathilde Caron et al. / Emerging Properties in Self-Supervised Vision Transformers / ICCV 2021
- [DINO: Emerging Properties in Self-Supervised Vision Transformers \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-dino-kor.md): Mathilde Caron et al. / Emerging Properties in Self-Supervised Vision Transformers / ICCV 2021
- [DatasetGAN \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-datasetgan-eng.md): Zhang et al. / DatasetGAN - Efficient Labeled Data Factory with Minimal Human Effort / CVPR 2021
- [MOS \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-mos-kor.md): Huang et al. / MOS; Towards Scaling Out-of-distribution Detection for Large Semantic Space / CVPR 2021
- [MOS \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-mos-eng.md): Huang et al. / MOS; Towards Scaling Out-of-distribution Detection for Large Semantic Space / CVPR 2021
- [PlaNet \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/eccv-2016-planet-eng.md): Weyand et al. / PlaNet - Photo Geolocation with Convolutional Neural Networks / ECCV 2016
- [MAE \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/fair-2021-mae-kor.md): Kaiming He, Xinlei Chen / Masked Autoencoders Are Scalable Vision Learners / Facebook AI Research(FAIR) 2021
- [Fair Attribute Classification through Latent Space De-biasing \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-latentspacedebiasing-kor.md): Vikram V. Ramaswamy / Fair Attribute Classification through Latent Space De-biasing / CVPR 2021 Oral
- [Fair Attribute Classification through Latent Space De-biasing \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-latentspacedebiasing-eng.md): Vikram V. Ramaswamy / Fair Attribute Classification through Latent Space De-biasing / CVPR 2021 Oral
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iclr-2019-mbmrl-kor.md): Nagabandi, Clavera / Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning / ICLR 2019
- [PointNet \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2017-pointnet-kor.md): Qi et al. / PointNet; Deep Learning on Point Sets for 3D Classification and Segmentation / CVPR 2020
- [PointNet \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2017-pointnet-eng.md): Qi et al. / PointNet; Deep Learning on Point Sets for 3D Classification and Segmentation / CVPR 2020
- [MSD AT \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/neuralnetworks-2022-fast-at-kor.md): Chen et al./ Towards improving fast adversarial training in multi-exit network / Neural Networks 2022
- [MM-TTA \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-mmtta-kor.md): Shin et al. / MM-TTA / CVPR 2022
- [MM-TTA \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2022-mmtta-eng.md): Shin et al. / MM-TTA / CVPR 2022
- [M-CAM \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/bmvc-2021-mcam-eng.md): Kim et al. / M-CAM - Visual Explanation of Challenging Conditioned Dataset with Bias-reducing Memory / BMVC 2021
- [MipNerF \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/iccv-2021-mipnerf-kor.md): (Description) Jonathan T. Barron et al. / Mip-Nerf - A Multiscale Representation for Anti-Aliasing Neural Radiance Fields / ICCV 2021
- [The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/neurips-2021-learning-zero-shot-segmentation-from-videos.md): Liu et al. / The Emergence of Objectness- Learning Zero-Shot Segmentation from Videos / NeurIPS 2021
- [Calibration \[Eng\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/neurips-2021-calibration-eng.md): (Description) Matthias Minderer / Revisiting the calibration of modern neural networks / NeurIPS 2021
- [CenterPoint \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/cvpr-2021-centerpoint-kor.md): Yin et al. / Center-based 3D Object Detection and Tracking / CVPR 2021
- [YOLOX \[Kor\]](https://awesome-davian.gitbook.io/awesome-reviews/paper-review/2022-spring-paper-review/arxiv-2021-yolox-kor.md): Ge et al / YOLOX; Exceeding YOLO Series in 2021 / ArXiv 2021


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