1.CVPR2021接受论文&&代码&&Post&&PPT (持续更新中，敬请关注)
28.D-NeRF: Neural Radiance Fields for Dynamic Scenes(D-NeRF：动态场景的神经辐射场) project
27.Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(样式编码：用于图像到图像翻译的StyleGAN编码器)
project
26.A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿势面部识别的3D GAN)
paper
25.Multi-Stage Progressive Image Restoration
paper
24.Rotation Equivariant Siamese Networks for Tracking（旋转等距连体网络进行跟踪）
paper
23.Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning(探索少量学习的不变表示形式和等变表示形式的互补强度)
22.Open-world object detection(开放世界中的目标检测)
code
21.Multi-Stage Progressive Image Restoration(多阶段渐进式图像复原)
paper|code
20.Weakly Supervised Learning of Rigid 3D Scene Flow(刚性3D场景流的弱监督学习)
paper|code|project
19.PREDATOR: Registration of 3D Point Clouds with Low Overlap(预测器：低重叠的3D点云的注册)
paper|code|project
18.Sequential Graph Convolutional Network for Active Learning(主动学习的顺序图卷积网络)
paper
17.Multiresolution Knowledge Distillation for Anomaly Detection(用于异常检测的多分辨率知识蒸馏)
paper
16.Positive-Unlabeled Data Purification in the Wild for Object Detection
15.Data-Free Knowledge Distillation For Image Super-Resolution(DAFL算法的SR版本)
14.Manifold Regularized Dynamic Network Pruning（动态剪枝的过程中考虑样本复杂度与网络复杂度的约束）
13.Distilling Object Detectors via Decoupled Features（前景背景分离的蒸馏技术）
12.Inverting the Inherence of Convolution for Visual Recognition
11.Representative Batch Normalization with Feature Calibration
10.PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
9.Learning the Superpixel in a Non-iterative and Lifelong Manner
8.RepVGG: Making VGG-style ConvNets Great Again
paper|code
解读：RepVGG：极简架构，SOTA性能，让VGG式模型再次伟大
7.Transformer Interpretability Beyond Attention Visualization
paper
6.UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper
解读：无监督预训练检测器
5.Pre-Trained Image Processing Transformer(底层视觉预训练模型)
paper
4.ReNAS: Relativistic Evaluation of Neural Architecture Search(NAS predictor当中ranking loss的重要性)
paper
3.AdderSR: Towards Energy Efficient Image Super-Resolution(将加法网路应用到图像超分辨率中)
paper|code
解读：华为开源加法神经网络
2.Learning Student Networks in the Wild（一种不需要原始训练数据的模型压缩和加速技术）
paper|code
解读：华为诺亚方舟实验室提出无需数据网络压缩技术
1.HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens（降低NAS的成本）
paper
论文合集下载
链接：https://pan.baidu.com/s/1caJ8PKQizDYt1TInEVBjSg
提取码：uuro
下载链接和提取码已更新（2021-03-02）
PS：整理太累，请尽快收藏或下载，防止过期 ~ -