【学术相关】CVPR2021最新接收论文合集！22个方向100+篇论文汇总｜持续更新
報(bào)道丨極市平臺
導(dǎo)讀
?CVPR2021結(jié)果已出，本文為CVPR最新接收論文的資源匯總貼，附有相關(guān)文章與代碼鏈接。?
文章在Github上持續(xù)更新，歡迎大家 star/fork（點(diǎn)擊閱讀原文即可跳轉(zhuǎn)）：
https://github.com/extreme-assistant/CVPR2021-Paper-Code-Interpretation/blob/master/CVPR2021.md
官網(wǎng)鏈接：http://cvpr2021.thecvf.com
時間：2021年6月19日-6月25日
論文接收公布時間：2021年2月28日
1.CVPR2021接受論文/代碼分方向整理
分類目錄：
1. 檢測
圖像目標(biāo)檢測(Image Object Detection)
視頻目標(biāo)檢測(Video Object Detection)
三維目標(biāo)檢測(3D Object Detection)
動作檢測(Activity Detection)
異常檢測(Anomally Detetion)
2. 圖像分割(Image Segmentation)
全景分割(Panoptic Segmentation)
語義分割(Semantic Segmentation)
實(shí)例分割(Instance Segmentation)
摳圖(Matting)
3. 圖像處理(Image Processing)
圖像復(fù)原(Image Restoration)/超分辨率(Super Resolution)
圖像陰影去除(Image Shadow Removal)
圖像去噪/去模糊/去雨去霧(Image Denoising)
圖像編輯(Image Edit)
圖像翻譯(Image Translation))
4. 人臉(Face)
5. 目標(biāo)跟蹤(Object Tracking)
6. 重識別(Re-Identification)
7. 醫(yī)學(xué)影像(Medical Imaging)
8. GAN/生成式/對抗式(GAN/Generative/Adversarial)
9. 估計(jì)(Estimation)
人體姿態(tài)估計(jì)(Human Pose Estimation)
光流/位姿/運(yùn)動估計(jì)(Flow/Pose/Motion Estimation)
深度估計(jì)(Depth Estimation)
10. 三維視覺(3D Vision)
三維點(diǎn)云(3D Point Cloud)
三維重建(3D Reconstruction)
11. 神經(jīng)網(wǎng)絡(luò)架構(gòu)(Neural Network Structure)
Transformer
圖神經(jīng)網(wǎng)絡(luò)(GNN)
12. 神經(jīng)網(wǎng)絡(luò)架構(gòu)搜索(NAS)
13. 數(shù)據(jù)處理(Data Processing)
數(shù)據(jù)增廣(Data Augmentation)
歸一化/正則化(Batch Normalization)
圖像聚類(Image Clustering)
14. 模型壓縮(Model Compression)
知識蒸餾(Knowledge Distillation)
15. 模型評估(Model Evaluation)
16. 數(shù)據(jù)集(Database)
17. 主動學(xué)習(xí)(Active Learning)
18. 小樣本學(xué)習(xí)/零樣本(Few-shot Learning)
19. 持續(xù)學(xué)習(xí)(Continual Learning/Life-long Learning)
20. 視覺推理(Visual Reasoning)
21. 遷移學(xué)習(xí)/domain/自適應(yīng)
22. 對比學(xué)習(xí)(Contrastive Learning)
暫無分類
檢測
圖像目標(biāo)檢測(Image Object Detection)
[7] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小樣本目標(biāo)檢測的語義關(guān)系推理)
paper：https://arxiv.org/abs/2103.01903
[6] General Instance Distillation for Object Detection(通用實(shí)例蒸餾技術(shù)在目標(biāo)檢測中的應(yīng)用)
paper：https://arxiv.org/abs/2103.02340
[5] Instance Localization for Self-supervised Detection Pretraining(自監(jiān)督檢測預(yù)訓(xùn)練的實(shí)例定位)
paper：https://arxiv.org/pdf/2102.08318.pdf
code：https://github.com/limbo0000/InstanceLoc
[4] Multiple Instance Active Learning for Object Detection（用于對象檢測的多實(shí)例主動學(xué)習(xí)）
paper：https://github.com/yuantn/MIAL/raw/master/paper.pdf
code：https://github.com/yuantn/MIAL
[3] Towards Open World Object Detection(開放世界中的目標(biāo)檢測)
paper：Towards Open World Object Detection
code：https://github.com/JosephKJ/OWOD
[2] Positive-Unlabeled Data Purification in the Wild for Object Detection(野外檢測對象的陽性無標(biāo)簽數(shù)據(jù)提純)
[1] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper：https://arxiv.org/pdf/2011.09094.pdf
解讀：
無監(jiān)督預(yù)訓(xùn)練檢測器：https://www.zhihu.com/question/432321109/answer/1606004872
視頻目標(biāo)檢測(Video Object Detection)
[3] Depth from Camera Motion and Object Detection(相機(jī)運(yùn)動和物體檢測的深度)
paper：https://arxiv.org/abs/2103.01468
[2] There is More than Meets the Eye: Self-Supervised Multi-Object Detection ?and Tracking with Sound by Distilling Multimodal Knowledge(多模態(tài)知識提取的自監(jiān)督多目標(biāo)檢測與有聲跟蹤)
paper：https://arxiv.org/abs/2103.01353
project：http://rl.uni-freiburg.de/research/multimodal-distill
[1] Dogfight: Detecting Drones from Drone Videos（從無人機(jī)視頻中檢測無人機(jī)）
三維目標(biāo)檢測(3D object detection)
[2] 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection(利用IoU預(yù)測進(jìn)行半監(jiān)督3D對象檢測)
paper：https://arxiv.org/pdf/2012.04355.pdf
code：https://github.com/THU17cyz/3DIoUMatch
project：https://thu17cyz.github.io/3DIoUMatch/
video：https://youtu.be/nuARjhkQN2U
[1] Categorical Depth Distribution Network for Monocular 3D Object Detection(用于單目三維目標(biāo)檢測的分類深度分布網(wǎng)絡(luò))
paper：https://arxiv.org/abs/2103.01100
動作檢測(Activity Detection)
[1] Coarse-Fine Networks for Temporal Activity Detection in Videos
paper：https://arxiv.org/abs/2103.01302
異常檢測(Anomally Detetion)
[1] Multiresolution Knowledge Distillation for Anomaly Detection(用于異常檢測的多分辨率知識蒸餾)
paper：https://arxiv.org/abs/2011.11108
圖像分割(Image Segmentation)
[2] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
paper：https://arxiv.org/abs/2012.06166
code：https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
[1] PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation(語義流經(jīng)點(diǎn)以進(jìn)行航空圖像分割)
全景分割(Panoptic Segmentation)
[2] Cross-View Regularization for Domain Adaptive Panoptic Segmentation(用于域自適應(yīng)全景分割的跨視圖正則化)
paper：https://arxiv.org/abs/2103.02584
[1] 4D Panoptic LiDAR Segmentation（4D全景LiDAR分割）
paper：https://arxiv.org/abs/2102.12472
語義分割(Semantic Segmentation)
[2] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市規(guī)模3D點(diǎn)云的語義分割：數(shù)據(jù)集，基準(zhǔn)和挑戰(zhàn))
paper：https://arxiv.org/abs/2009.03137
code：https://github.com/QingyongHu/SensatUrban
[1] PLOP: Learning without Forgetting for Continual Semantic Segmentation（PLOP：學(xué)習(xí)而不會忘記連續(xù)的語義分割）
paper：https://arxiv.org/abs/2011.11390
實(shí)例分割(Instance Segmentation)
[1] End-to-End Video Instance Segmentation with Transformers(使用Transformer的端到端視頻實(shí)例分割)
paper：https://arxiv.org/abs/2011.14503
摳圖(Matting)
[1] Real-Time High Resolution Background Matting
paper：https://arxiv.org/abs/2012.07810
code：https://github.com/PeterL1n/BackgroundMattingV2
project：https://grail.cs.washington.edu/projects/background-matting-v2/
video：https://youtu.be/oMfPTeYDF9g
9. 估計(jì)(Estimation)
人體姿態(tài)估計(jì)(Human Pose Estimation)
[2] CanonPose: Self-supervised Monocular 3D Human Pose Estimation in the Wild（野外自監(jiān)督的單眼3D人類姿態(tài)估計(jì)）
[1] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers（具有透視作物層的3D姿勢的幾何感知神經(jīng)重建）
paper：https://arxiv.org/abs/2011.13607
光流/位姿/運(yùn)動估計(jì)(Flow/Pose/Motion Estimation)
[3] GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation(用于單眼6D對象姿態(tài)估計(jì)的幾何引導(dǎo)直接回歸網(wǎng)絡(luò))
paper：http://arxiv.org/abs/2102.12145
code：https://github.com/THU-DA-6D-Pose-Group/GDR-Net
[2] Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments(在動態(tài)室內(nèi)環(huán)境中，通過空間劃分的魯棒神經(jīng)路由可實(shí)現(xiàn)攝像機(jī)的重新定位)
paper：https://arxiv.org/abs/2012.04746
project：https://ai.stanford.edu/~hewang/
[1] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通過3D掃描同步進(jìn)行多主體分割和運(yùn)動估計(jì))
paper：https://arxiv.org/pdf/2101.06605.pdf
code：https://github.com/huangjh-pub/multibody-sync
深度估計(jì)(Depth Estimation)
圖像處理(Image Processing)
圖像復(fù)原(Image Restoration)/超分辨率(Super Resolution)
[3] Multi-Stage Progressive Image Restoration(多階段漸進(jìn)式圖像復(fù)原)
paper：https://arxiv.org/abs/2102.02808
code：https://github.com/swz30/MPRNet
[2] Data-Free Knowledge Distillation For Image Super-Resolution(DAFL算法的SR版本)
[1] AdderSR: Towards Energy Efficient Image Super-Resolution(將加法網(wǎng)路應(yīng)用到圖像超分辨率中)
paper：https://arxiv.org/pdf/2009.08891.pdf
code：https://github.com/huawei-noah/AdderNet
解讀：華為開源加法神經(jīng)網(wǎng)絡(luò)
圖像陰影去除(Image Shadow Removal)
[1] Auto-Exposure Fusion for Single-Image Shadow Removal(用于單幅圖像陰影去除的自動曝光融合)
paper：https://arxiv.org/abs/2103.01255
code：https://github.com/tsingqguo/exposure-fusion-shadow-removal
圖像去噪/去模糊/去雨去霧(Image Denoising)
[1] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects(快速移動物體的去模糊和形狀恢復(fù))
paper：https://arxiv.org/abs/2012.00595
code：https://github.com/rozumden/DeFMO
video：https://www.youtube.com/watch?v=pmAynZvaaQ4
圖像編輯(Image Edit)
[1] Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing（利用GAN中潛在的空間維度進(jìn)行實(shí)時圖像編輯）
圖像翻譯（Image Translation）
[2] Image-to-image Translation via Hierarchical Style Disentanglement
paper：https://arxiv.org/abs/2103.01456
code：https://github.com/imlixinyang/HiSD
[1] Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(樣式編碼：用于圖像到圖像翻譯的StyleGAN編碼器)
paper：https://arxiv.org/abs/2008.00951
code：https://github.com/eladrich/pixel2style2pixel
project：https://eladrich.github.io/pixel2style2pixel/
人臉(Face)
[5] Cross Modal Focal Loss for RGBD Face Anti-Spoofing(Cross Modal Focal Loss for RGBD Face Anti-Spoofing)?
paper：https://arxiv.org/abs/2103.00948
[4] When Age-Invariant Face Recognition Meets Face Age Synthesis: A ?Multi-Task Learning Framework(當(dāng)年齡不變的人臉識別遇到人臉年齡合成時：一個多任務(wù)學(xué)習(xí)框架)
paper：https://arxiv.org/abs/2103.01520
code：https://github.com/Hzzone/MTLFace
[3] Multi-attentional Deepfake Detection(多注意的深偽檢測)
paper：https://arxiv.org/abs/2103.02406
[2] Image-to-image Translation via Hierarchical Style Disentanglement
paper：https://arxiv.org/abs/2103.01456
code：https://github.com/imlixinyang/HiSD
[1] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿勢面部識別的3D GAN)
paper：https://arxiv.org/pdf/2012.10545.pdf
目標(biāo)跟蹤(Object Tracking)
[4] HPS: localizing and tracking people in large 3D scenes from wearable sensors(通過可穿戴式傳感器對大型3D場景中的人進(jìn)行定位和跟蹤)
[3] Track to Detect and Segment: An Online Multi-Object Tracker(跟蹤檢測和分段：在線多對象跟蹤器)
project：https://jialianwu.com/projects/TraDeS.html
video：https://www.youtube.com/watch?v=oGNtSFHRZJA
[2] Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking(多目標(biāo)跟蹤的概率小波計(jì)分和修復(fù))
paper：https://arxiv.org/abs/2012.02337
[1] Rotation Equivariant Siamese Networks for Tracking（旋轉(zhuǎn)等距連體網(wǎng)絡(luò)進(jìn)行跟蹤）
paper：https://arxiv.org/abs/2012.13078
重識別
[1] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批處理人員重新標(biāo)識的元批實(shí)例規(guī)范化)
paper：https://arxiv.org/abs/2011.14670
醫(yī)學(xué)影像(Medical Imaging)
[4] Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning(多機(jī)構(gòu)協(xié)作改進(jìn)基于深度學(xué)習(xí)的聯(lián)合學(xué)習(xí)磁共振圖像重建)
paper：https://arxiv.org/abs/2103.02148
code：https://github.com/guopengf/FLMRCM
[3] 3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management(用于胰腺腫塊分割，診斷和定量患者管理的3D圖形解剖學(xué)幾何集成網(wǎng)絡(luò))
[2] Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies(深部病變追蹤器：在4D縱向成像研究中監(jiān)控病變)
paper：https://arxiv.org/abs/2012.04872
[1] Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization(通過脊柱矯正和解剖學(xué)約束優(yōu)化在CT中自動進(jìn)行椎骨定位和識別)
paper：https://arxiv.org/abs/2012.07947
神經(jīng)網(wǎng)絡(luò)架構(gòu)搜索(NAS)
[3] AttentiveNAS: Improving Neural Architecture Search via Attentive(通過注意力改善神經(jīng)架構(gòu)搜索)
paper：https://arxiv.org/pdf/2011.09011.pdf
[2] ReNAS: Relativistic Evaluation of Neural Architecture Search(NAS predictor當(dāng)中ranking loss的重要性)
paper：https://arxiv.org/pdf/1910.01523.pdf
[1] HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens（降低NAS的成本）
paper：https://arxiv.org/pdf/2005.14446.pdf
GAN/生成式/對抗式(GAN/Generative/Adversarial)
[5] Efficient Conditional GAN Transfer with Knowledge Propagation across Classes(高效的有條件GAN轉(zhuǎn)移以及跨課程的知識傳播)
paper：https://arxiv.org/abs/2102.06696
code：http://github.com/mshahbazi72/cGANTransfer
[4] Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing（利用GAN中潛在的空間維度進(jìn)行實(shí)時圖像編輯）
[3] Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs(Hijack-GAN：意外使用經(jīng)過預(yù)訓(xùn)練的黑匣子GAN)
paper：https://arxiv.org/pdf/2011.14107.pdf
[2] Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation(樣式編碼：用于圖像到圖像翻譯的StyleGAN編碼器)
paper：https://arxiv.org/abs/2008.00951
code：https://github.com/eladrich/pixel2style2pixel
project：https://eladrich.github.io/pixel2style2pixel/
[1] A 3D GAN for Improved Large-pose Facial Recognition(用于改善大姿勢面部識別的3D GAN)
paper：https://arxiv.org/pdf/2012.10545.pdf
三維視覺(3D Vision)
[2] A Deep Emulator for Secondary Motion of 3D Characters(三維角色二次運(yùn)動的深度仿真器)
paper：https://arxiv.org/abs/2103.01261
[1] 3D CNNs with Adaptive Temporal Feature Resolutions(具有自適應(yīng)時間特征分辨率的3D CNN)
paper：https://arxiv.org/abs/2011.08652
三維點(diǎn)云(3D Point Cloud)
[6] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市規(guī)模3D點(diǎn)云的語義分割：數(shù)據(jù)集，基準(zhǔn)和挑戰(zhàn))
paper：https://arxiv.org/abs/2009.03137
code：https://github.com/QingyongHu/SensatUrban
[5] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration(SpinNet：學(xué)習(xí)用于3D點(diǎn)云注冊的通用表面描述符)
paper：https://t.co/xIAWVGQeB2?amp=1
code：https://github.com/QingyongHu/SpinNet
[4] MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization(通過3D掃描同步進(jìn)行多主體分割和運(yùn)動估計(jì))
paper：https://arxiv.org/pdf/2101.06605.pdf
code：https://github.com/huangjh-pub/multibody-sync
[3] Diffusion Probabilistic Models for 3D Point Cloud Generation(三維點(diǎn)云生成的擴(kuò)散概率模型)
paper：https://arxiv.org/abs/2103.01458
code：https://github.com/luost26/diffusion-point-cloud
[2] Style-based Point Generator with Adversarial Rendering for Point Cloud Completion(用于點(diǎn)云補(bǔ)全的對抗性渲染基于樣式的點(diǎn)生成器)
paper：https://arxiv.org/abs/2103.02535
[1] PREDATOR: Registration of 3D Point Clouds with Low Overlap(預(yù)測器：低重疊的3D點(diǎn)云的注冊)
paper：https://arxiv.org/pdf/2011.13005.pdf
code：https://github.com/ShengyuH/OverlapPredator
project：https://overlappredator.github.io/
三維重建(3D Reconstruction)
[1] PCLs: Geometry-aware Neural Reconstruction of 3D Pose with Perspective Crop Layers（具有透視作物層的3D姿勢的幾何感知神經(jīng)重建）
paper：https://arxiv.org/abs/2011.13607
模型壓縮(Model Compression)
[2] Manifold Regularized Dynamic Network Pruning（動態(tài)剪枝的過程中考慮樣本復(fù)雜度與網(wǎng)絡(luò)復(fù)雜度的約束）
[1] Learning Student Networks in the Wild（一種不需要原始訓(xùn)練數(shù)據(jù)的模型壓縮和加速技術(shù)）
paper：https://arxiv.org/pdf/1904.01186.pdf
code：https://github.com/huawei-noah/DAFL
解讀：
華為諾亞方舟實(shí)驗(yàn)室提出無需數(shù)據(jù)網(wǎng)絡(luò)壓縮技術(shù)：https://zhuanlan.zhihu.com/p/81277796
知識蒸餾(Knowledge Distillation)
[3] General Instance Distillation for Object Detection(通用實(shí)例蒸餾技術(shù)在目標(biāo)檢測中的應(yīng)用)
paper:https://arxiv.org/abs/2103.02340
[2] Multiresolution Knowledge Distillation for Anomaly Detection(用于異常檢測的多分辨率知識蒸餾)
paper:https://arxiv.org/abs/2011.11108
[1] Distilling Object Detectors via Decoupled Features（前景背景分離的蒸餾技術(shù)）
神經(jīng)網(wǎng)絡(luò)架構(gòu)(Neural Network Structure)
[3] Rethinking Channel Dimensions for Efficient Model Design(重新考慮通道尺寸以進(jìn)行有效的模型設(shè)計(jì))
paper:https://arxiv.org/abs/2007.00992
code:https://github.com/clovaai/rexnet
[2] Inverting the Inherence of Convolution for Visual Recognition（顛倒卷積的固有性以進(jìn)行視覺識別）
[1] RepVGG: Making VGG-style ConvNets Great Again
paper:https://arxiv.org/abs/2101.03697
code:https://github.com/megvii-model/RepVGG
解讀：
RepVGG：極簡架構(gòu)，SOTA性能，讓VGG式模型再次偉大:https://zhuanlan.zhihu.com/p/344324470
Transformer
[3] Transformer Interpretability Beyond Attention Visualization(注意力可視化之外的Transformer可解釋性)
paper:https://arxiv.org/pdf/2012.09838.pdf
code:https://github.com/hila-chefer/Transformer-Explainability
[2] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
paper:https://arxiv.org/pdf/2011.09094.pdf
解讀：無監(jiān)督預(yù)訓(xùn)練檢測器:https://www.zhihu.com/question/432321109/answer/1606004872
[1] Pre-Trained Image Processing Transformer(底層視覺預(yù)訓(xùn)練模型)
paper:https://arxiv.org/pdf/2012.00364.pdf
圖神經(jīng)網(wǎng)絡(luò)(GNN)
[2] Quantifying Explainers of Graph Neural Networks in Computational Pathology(計(jì)算病理學(xué)中圖神經(jīng)網(wǎng)絡(luò)的量化解釋器)
paper:https://arxiv.org/pdf/2011.12646.pdf
[1] Sequential Graph Convolutional Network for Active Learning(主動學(xué)習(xí)的順序圖卷積網(wǎng)絡(luò))
paper:https://arxiv.org/pdf/2006.10219.pdf
數(shù)據(jù)處理(Data Processing)
數(shù)據(jù)增廣(Data Augmentation)
[1] KeepAugment: A Simple Information-Preserving Data Augmentation(一種簡單的保存信息的數(shù)據(jù)擴(kuò)充)
paper:https://arxiv.org/pdf/2011.11778.pdf
歸一化/正則化(Batch Normalization)
[3] Adaptive Consistency Regularization for Semi-Supervised Transfer Learning(半監(jiān)督轉(zhuǎn)移學(xué)習(xí)的自適應(yīng)一致性正則化)
paper:https://arxiv.org/abs/2103.02193
code:https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning
[2] Meta Batch-Instance Normalization for Generalizable Person Re-Identification(通用批處理人員重新標(biāo)識的元批實(shí)例規(guī)范化)
paper:https://arxiv.org/abs/2011.14670
[1] Representative Batch Normalization with Feature Calibration（具有特征校準(zhǔn)功能的代表性批量歸一化）
圖像聚類(Image Clustering)
[2] Improving Unsupervised Image Clustering With Robust Learning（通過魯棒學(xué)習(xí)改善無監(jiān)督圖像聚類）
paper:https://arxiv.org/abs/2012.11150
code:https://github.com/deu30303/RUC
[1] Reconsidering Representation Alignment for Multi-view Clustering(重新考慮多視圖聚類的表示對齊方式)
模型評估(Model Evaluation)
[1] Are Labels Necessary for Classifier Accuracy Evaluation?(測試集沒有標(biāo)簽，我們可以拿來測試模型嗎？)
paper:https://arxiv.org/abs/2007.02915
解讀:https://zhuanlan.zhihu.com/p/328686799
數(shù)據(jù)集(Database)
[2] Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges(走向城市規(guī)模3D點(diǎn)云的語義分割：數(shù)據(jù)集，基準(zhǔn)和挑戰(zhàn))
paper:https://arxiv.org/abs/2009.03137
code:https://github.com/QingyongHu/SensatUrban
[1] Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels（重新標(biāo)記ImageNet：從單標(biāo)簽到多標(biāo)簽，從全局標(biāo)簽到本地標(biāo)簽）
paper:https://arxiv.org/abs/2101.05022
code:https://github.com/naver-ai/relabel_imagenet
主動學(xué)習(xí)(Active Learning)
[3] Vab-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
paper:https://github.com/yuantn/MIAL/raw/master/paper.pdf
code:https://github.com/yuantn/MIAL
[2] Multiple Instance Active Learning for Object Detection（用于對象檢測的多實(shí)例主動學(xué)習(xí)）
paper:https://github.com/yuantn/MIAL/raw/master/paper.pdf
code:https://github.com/yuantn/MIAL
[1] Sequential Graph Convolutional Network for Active Learning(主動學(xué)習(xí)的順序圖卷積網(wǎng)絡(luò))
paper:https://arxiv.org/pdf/2006.10219.pdf
小樣本學(xué)習(xí)(Few-shot Learning)/零樣本
[5] Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
paper:https://arxiv.org/abs/2012.06166
code:https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation
[4] Counterfactual Zero-Shot and Open-Set Visual Recognition(反事實(shí)零射和開集視覺識別)
paper:https://arxiv.org/abs/2103.00887
code:https://github.com/yue-zhongqi/gcm-cf
[3] Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection(小樣本目標(biāo)檢測的語義關(guān)系推理)
paper:https://arxiv.org/abs/2103.01903
[2] Few-shot Open-set Recognition by Transformation Consistency(轉(zhuǎn)換一致性很少的開放集識別)
[1] Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning(探索少量學(xué)習(xí)的不變表示形式和等變表示形式的互補(bǔ)強(qiáng)度)
paper:https://arxiv.org/abs/2103.01315
持續(xù)學(xué)習(xí)(Continual Learning/Life-long Learning)
[2] Rainbow Memory: Continual Learning with a Memory of Diverse Samples（不斷學(xué)習(xí)與多樣本的記憶）
[1] Learning the Superpixel in a Non-iterative and Lifelong Manner(以非迭代和終身的方式學(xué)習(xí)超像素)
視覺推理(Visual Reasoning)
[1] Transformation Driven Visual Reasoning(轉(zhuǎn)型驅(qū)動的視覺推理)
paper:https://arxiv.org/pdf/2011.13160.pdf
code:https://github.com/hughplay/TVR
project:https://hongxin2019.github.io/TVR/
遷移學(xué)習(xí)/domain/自適應(yīng)](#domain)
[4] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning(通過域隨機(jī)化和元學(xué)習(xí)對視覺表示進(jìn)行連續(xù)調(diào)整)
paper:https://arxiv.org/abs/2012.04324
[3] Domain Generalization via Inference-time Label-Preserving Target Projections(基于推理時間保標(biāo)目標(biāo)投影的區(qū)域泛化)
paper:https://arxiv.org/abs/2103.01134
[2] MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive ?Sensing(可伸縮的自適應(yīng)視頻壓縮傳感重建)
paper:https://arxiv.org/abs/2103.01786
code:https://github.com/xyvirtualgroup/MetaSCI-CVPR2021
[1] FSDR: Frequency Space Domain Randomization for Domain Generalization(用于域推廣的頻域隨機(jī)化)
paper:https://arxiv.org/abs/2103.02370
對比學(xué)習(xí)(Contrastive Learning)
[1] Fine-grained Angular Contrastive Learning with Coarse Labels(粗標(biāo)簽的細(xì)粒度角度對比學(xué)習(xí))
paper:https://arxiv.org/abs/2012.03515
暫無分類
Quantifying Explainers of Graph Neural Networks in Computational Pathology(計(jì)算病理學(xué)中圖神經(jīng)網(wǎng)絡(luò)的量化解釋器)
paper:https://arxiv.org/pdf/2011.12646.pdf
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts(探索具有對比場景上下文的數(shù)據(jù)高效3D場景理解)
paper:http://arxiv.org/abs/2012.09165
project:http://sekunde.github.io/project_efficient
video:http://youtu.be/E70xToZLgs4
Data-Free Model Extraction(無數(shù)據(jù)模型提取)
paper:https://arxiv.org/abs/2011.14779
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition(用于【位置識別】的局部全局描述符的【多尺度融合】)
paper:https://arxiv.org/pdf/2103.01486.pdf
code:https://github.com/QVPR/Patch-NetVLAD
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations(適用于正確概念的權(quán)利：通過可解釋性來修正神經(jīng)符號概念)
paper:https://arxiv.org/abs/2011.12854
Multi-Objective Interpolation Training for Robustness to Label Noise(多目標(biāo)插值訓(xùn)練的魯棒性)
paper:https://arxiv.org/abs/2012.04462
code:https://git.io/JI40X
VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs(【文本生成】VX2TEXT：基于視頻的文本生成的端到端學(xué)習(xí)來自多模式輸入)
paper:https://arxiv.org/pdf/2101.12059.pdf
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans(【圖像字幕】Scan2Cap：RGB-D掃描中的上下文感知密集字幕)?
paper:https://arxiv.org/abs/2012.02206
code:https://github.com/daveredrum/Scan2Cap
project:https://daveredrum.github.io/Scan2Cap/
video:https://youtu.be/AgmIpDbwTCY
Hierarchical and Partially Observable Goal-driven Policy Learning with ?Goals Relational Graph(基于目標(biāo)關(guān)系圖的分層部分可觀測目標(biāo)驅(qū)動策略學(xué)習(xí))
paper:https://arxiv.org/abs/2103.01350
ID-Unet: Iterative Soft and Hard Deformation for View Synthesis(視圖合成的迭代軟硬變形)
paper:https://arxiv.org/abs/2103.02264
PML: Progressive Margin Loss for Long-tailed Age Classification(【長尾分布】【圖像分類】長尾年齡分類的累進(jìn)邊際損失)
paper:https://arxiv.org/abs/2103.02140
Diversifying Sample Generation for Data-Free Quantization（【圖像生成】多樣化的樣本生成，實(shí)現(xiàn)無數(shù)據(jù)量化）
paper:https://arxiv.org/abs/2103.01049
Domain Generalization via Inference-time Label-Preserving Target Projections（通過保留推理時間的目標(biāo)投影進(jìn)行域泛化）
paper:https://arxiv.org/pdf/2103.01134.pdf
DeRF: Decomposed Radiance Fields（分解的輻射場）
project:https://ubc-vision.github.io/derf/
Densely connected multidilated convolutional networks for dense prediction tasks（【密集預(yù)測】密集連接的多重卷積網(wǎng)絡(luò)，用于密集的預(yù)測任務(wù)）
paper:https://arxiv.org/abs/2011.11844
VirTex: Learning Visual Representations from Textual Annotations（【表示學(xué)習(xí)】從文本注釋中學(xué)習(xí)視覺表示）
paper:https://arxiv.org/abs/2006.06666
code:https://github.com/kdexd/virtex
Weakly-supervised Grounded Visual Question Answering using Capsules（使用膠囊進(jìn)行弱監(jiān)督的地面視覺問答）
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation（【視頻插幀】FLAVR：用于快速幀插值的與流無關(guān)的視頻表示）
paper:https://arxiv.org/pdf/2012.08512.pdf
code:https://tarun005.github.io/FLAVR/Code
project:https://tarun005.github.io/FLAVR/
Probabilistic Embeddings for Cross-Modal Retrieval（跨模態(tài)檢索的概率嵌入）
paper:https://arxiv.org/abs/2101.05068
Self-supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map(道路動力學(xué)和成本圖的自監(jiān)督式多步同時預(yù)測)
IIRC: Incremental Implicitly-Refined Classification(增量式隱式定義的分類)
paper:https://arxiv.org/abs/2012.12477
project:https://chandar-lab.github.io/IIRC/
Fair Attribute Classification through Latent Space De-biasing(通過潛在空間去偏的公平屬性分類)
paper:https://arxiv.org/abs/2012.01469
code:https://github.com/princetonvisualai/gan-debiasing
project:https://princetonvisualai.github.io/gan-debiasing/
Information-Theoretic Segmentation by Inpainting Error Maximization(修復(fù)誤差最大化的信息理論分割)
paper:https://arxiv.org/abs/2012.07287
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pretraining(【視頻語言學(xué)習(xí)】UC2：通用跨語言跨模態(tài)視覺和語言預(yù)培訓(xùn))
Less is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling(通過稀疏采樣進(jìn)行視頻和語言學(xué)習(xí))
paper:https://arxiv.org/pdf/2102.06183.pdf
code:https://github.com/jayleicn/ClipBERT
D-NeRF: Neural Radiance Fields for Dynamic Scenes(D-NeRF：動態(tài)場景的神經(jīng)輻射場)
paper:https://arxiv.org/abs/2011.13961
project:https://www.albertpumarola.com/research/D-NeRF/index.html
Weakly Supervised Learning of Rigid 3D Scene Flow(剛性3D場景流的弱監(jiān)督學(xué)習(xí))
paper:https://arxiv.org/pdf/2102.08945.pdf
code:https://arxiv.org/pdf/2102.08945.pdf
project:https://3dsceneflow.github.io/
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