Girshick, R.
Standard Author Listing
with: Adcock, A.: effectiveness of MAE pre-pretraining for billion-scale pre...
with: Adcock, A.: Revisiting Weakly Supervised Pre-Training of Visual Percep...
with: Aggarwal, V.: effectiveness of MAE pre-pretraining for billion-scale p...
with: Agrawal, P.: Analyzing the Performance of Multilayer Neural Networks f...
with: Althoff, T.: Sparselet Models for Efficient Multiclass Object Detection
with: Alwala, K.V.: effectiveness of MAE pre-pretraining for billion-scale p...
with: Arbelaez, P.: Aligning 3D models to RGB-D images of cluttered scenes
with: Arbelaez, P.: Hypercolumns for object segmentation and fine-grained lo...
with: Arbelaez, P.: Indoor Scene Understanding with RGB-D Images: Bottom-up ...
with: Arbelaez, P.: Learning Rich Features from RGB-D Images for Object Dete...
with: Arbelaez, P.: Object Instance Segmentation and Fine-Grained Localizati...
with: Arbelaez, P.: Perceptual Organization and Recognition of Indoor Scenes...
with: Arbelaez, P.: Simultaneous Detection and Segmentation
with: Arbelaez, P.: three R's of computer vision: Recognition, reconstructio...
with: Bala, K.: Inside-Outside Net: Detecting Objects in Context with Skip P...
with: Bell, S.: Inside-Outside Net: Detecting Objects in Context with Skip P...
with: Belongie, S.: Feature Pyramid Networks for Object Detection
with: Berg, A.C.: Boundary IoU: Improving Object-Centric Image Segmentation ...
with: Berg, A.C.: Segment Anything
with: Bharambe, A.: Exploring the Limits of Weakly Supervised Pretraining
with: Blake, A.: Efficient Human Pose Estimation from Single Depth Images
with: Blaschko, M.B.: Understanding Objects in Detail with Fine-Grained Attr...
with: Carreira, J.: three R's of computer vision: Recognition, reconstructio...
with: Chen, X.: TensorMask: A Foundation for Dense Object Segmentation
with: Chen, X.L.: Masked Autoencoders Are Scalable Vision Learners
with: Cheng, B.: Boundary IoU: Improving Object-Centric Image Segmentation E...
with: Cook, M.: Efficient Human Pose Estimation from Single Depth Images
with: Criminisi, A.: Efficient Human Pose Estimation from Single Depth Images
with: Criminisi, A.: Efficient Regression of General-Activity Human Poses fr...
with: Darrell, T.J.: Deformable part models are convolutional neural networks
with: Darrell, T.J.: Generalized Sparselet Models for Real-Time Multiclass O...
with: Darrell, T.J.: Learning Features by Watching Objects Move
with: Darrell, T.J.: Learning to Segment Every Thing
with: Darrell, T.J.: Part-Based R-CNNs for Fine-Grained Category Detection
with: Darrell, T.J.: Region-Based Convolutional Networks for Accurate Object...
with: Darrell, T.J.: Rich Feature Hierarchies for Accurate Object Detection ...
with: Darrell, T.J.: Sparselet Models for Efficient Multiclass Object Detect...
with: de Freitas Reis, V.: Revisiting Weakly Supervised Pre-Training of Visu...
with: Divvala, S.: You Only Look Once: Unified, Real-Time Object Detection
with: Dollar, P.: Aggregated Residual Transformations for Deep Neural Networks
with: Dollar, P.: Are Labels Necessary for Neural Architecture Search?
with: Dollar, P.: Boundary IoU: Improving Object-Centric Image Segmentation ...
with: Dollar, P.: Data Distillation: Towards Omni-Supervised Learning
with: Dollar, P.: Designing Network Design Spaces
with: Dollar, P.: Detecting and Recognizing Human-Object Interactions
with: Dollar, P.: effectiveness of MAE pre-pretraining for billion-scale pre...
with: Dollar, P.: Fast and Accurate Model Scaling
with: Dollar, P.: Feature Pyramid Networks for Object Detection
with: Dollar, P.: Focal Loss for Dense Object Detection
with: Dollar, P.: Learning Features by Watching Objects Move
with: Dollar, P.: Learning to Segment Every Thing
with: Dollar, P.: LVIS: A Dataset for Large Vocabulary Instance Segmentation
with: Dollar, P.: Mask R-CNN
with: Dollar, P.: Masked Autoencoders Are Scalable Vision Learners
with: Dollar, P.: Panoptic Feature Pyramid Networks
with: Dollar, P.: Panoptic Segmentation
with: Dollar, P.: Rethinking ImageNet Pre-Training
with: Dollar, P.: Revisiting Weakly Supervised Pre-Training of Visual Percep...
with: Dollar, P.: Segment Anything
with: Dollar, P.: TensorMask: A Foundation for Dense Object Segmentation
with: Donahue, J.: Part-Based R-CNNs for Fine-Grained Category Detection
with: Donahue, J.: Region-Based Convolutional Networks for Accurate Object D...
with: Donahue, J.: Rich Feature Hierarchies for Accurate Object Detection an...
with: Duval, Q.: effectiveness of MAE pre-pretraining for billion-scale pret...
with: Fan, H.: Long-Term Feature Banks for Detailed Video Understanding
with: Fan, H.Q.: effectiveness of MAE pre-pretraining for billion-scale pret...
with: Fan, H.Q.: Large-Scale Study on Unsupervised Spatiotemporal Representa...
with: Fan, H.Q.: Momentum Contrast for Unsupervised Visual Representation Le...
with: Farhadi, A.: Deep3D: Fully Automatic 2D-to-3D Video Conversion with De...
with: Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection
with: Fei Fei, L.: CLEVR: A Diagnostic Dataset for Compositional Language an...
with: Fei Fei, L.: Inferring and Executing Programs for Visual Reasoning
with: Feichtenhofer, C.: effectiveness of MAE pre-pretraining for billion-sc...
with: Feichtenhofer, C.: Large-Scale Study on Unsupervised Spatiotemporal Re...
with: Feichtenhofer, C.: Long-Term Feature Banks for Detailed Video Understa...
with: Feichtenhofer, C.: Multigrid Method for Efficiently Training Video Mod...
with: Felzenszwalb, P.F.: Generalized Sparselet Models for Real-Time Multicl...
with: Felzenszwalb, P.F.: Sparselet Models for Efficient Multiclass Object D...
with: Fergus, R.: Learning by Asking Questions
with: Finocchio, M.: Efficient Human Pose Estimation from Single Depth Images
with: Fitzgibbon, A.W.: Efficient Human Pose Estimation from Single Depth Im...
with: Fitzgibbon, A.W.: Efficient Regression of General-Activity Human Poses...
with: Fragkiadaki, K.: three R's of computer vision: Recognition, reconstruc...
with: Fritz, M.: Sparselet Models for Efficient Multiclass Object Detection
with: Gedik, B.: Revisiting Weakly Supervised Pre-Training of Visual Percept...
with: Geyer, C.: Generalized Sparselet Models for Real-Time Multiclass Objec...
with: Geyer, C.: Sparselet Models for Efficient Multiclass Object Detection
with: Girdhar, R.: effectiveness of MAE pre-pretraining for billion-scale pr...
with: Gkioxari, G.: Actions and Attributes from Wholes and Parts
with: Gkioxari, G.: Contextual Action Recognition with R*CNN
with: Gkioxari, G.: Data Distillation: Towards Omni-Supervised Learning
with: Gkioxari, G.: Detecting and Recognizing Human-Object Interactions
with: Gkioxari, G.: Mask R-CNN
with: Gkioxari, G.: three R's of computer vision: Recognition, reconstructio...
with: Gkioxari, G.: Using k-Poselets for Detecting People and Localizing The...
with: Goyal, P.: Focal Loss for Dense Object Detection
with: Gupta, A.: Learning by Asking Questions
with: Gupta, A.: LVIS: A Dataset for Large Vocabulary Instance Segmentation
with: Gupta, A.: Non-local Neural Networks
with: Gupta, A.: Training Region-Based Object Detectors with Online Hard Exa...
with: Gupta, S.: Aligning 3D models to RGB-D images of cluttered scenes
with: Gupta, S.: Indoor Scene Understanding with RGB-D Images: Bottom-up Seg...
with: Gupta, S.: Learning Rich Features from RGB-D Images for Object Detecti...
with: Gupta, S.: Perceptual Organization and Recognition of Indoor Scenes fr...
with: Gupta, S.: three R's of computer vision: Recognition, reconstruction a...
with: Gustafson, L.: Revisiting Weakly Supervised Pre-Training of Visual Per...
with: Gustafson, L.: Segment Anything
with: Hariharan, B.: CLEVR: A Diagnostic Dataset for Compositional Language ...
with: Hariharan, B.: Feature Pyramid Networks for Object Detection
with: Hariharan, B.: Hypercolumns for object segmentation and fine-grained l...
with: Hariharan, B.: Inferring and Executing Programs for Visual Reasoning
with: Hariharan, B.: Learning Features by Watching Objects Move
with: Hariharan, B.: Low-Shot Learning from Imaginary Data
with: Hariharan, B.: Low-Shot Visual Recognition by Shrinking and Hallucinat...
with: Hariharan, B.: Object Instance Segmentation and Fine-Grained Localizat...
with: Hariharan, B.: Simultaneous Detection and Segmentation
with: Hariharan, B.: three R's of computer vision: Recognition, reconstructi...
with: Hariharan, B.: Using k-Poselets for Detecting People and Localizing Th...
with: He, K.: Aggregated Residual Transformations for Deep Neural Networks
with: He, K.: Designing Network Design Spaces
with: He, K.: Detecting and Recognizing Human-Object Interactions
with: He, K.: Exploring Randomly Wired Neural Networks for Image Recognition
with: He, K.: Large-Scale Study on Unsupervised Spatiotemporal Representatio...
with: He, K.: Learning to Segment Every Thing
with: He, K.: Long-Term Feature Banks for Detailed Video Understanding
with: He, K.: Panoptic Segmentation
with: He, K.: PointRend: Image Segmentation As Rendering
with: He, K.: Rethinking ImageNet Pre-Training
with: He, K.: TensorMask: A Foundation for Dense Object Segmentation
with: He, K.M.: Are Labels Necessary for Neural Architecture Search?
with: He, K.M.: Data Distillation: Towards Omni-Supervised Learning
with: He, K.M.: Exploring Plain Vision Transformer Backbones for Object Dete...
with: He, K.M.: Exploring the Limits of Weakly Supervised Pretraining
with: He, K.M.: Faster R-CNN: Towards Real-Time Object Detection with Region...
with: He, K.M.: Feature Pyramid Networks for Object Detection
with: He, K.M.: Focal Loss for Dense Object Detection
with: He, K.M.: Mask R-CNN
with: He, K.M.: Masked Autoencoders Are Scalable Vision Learners
with: He, K.M.: Momentum Contrast for Unsupervised Visual Representation Lea...
with: He, K.M.: Multigrid Method for Efficiently Training Video Models, A
with: He, K.M.: Non-local Neural Networks
with: He, K.M.: Object Detection Networks on Convolutional Feature Maps
with: He, K.M.: Panoptic Feature Pyramid Networks
with: Hebert, M.: Learning by Asking Questions
with: Hebert, M.: Low-Shot Learning from Imaginary Data
with: Hoffman, J.: CLEVR: A Diagnostic Dataset for Compositional Language an...
with: Hoffman, J.: Inferring and Executing Programs for Visual Reasoning
with: Hu, R.: Learning to Segment Every Thing
with: Iandola, F.: Deformable part models are convolutional neural networks
with: Johnson, J.: CLEVR: A Diagnostic Dataset for Compositional Language an...
with: Johnson, J.: Inferring and Executing Programs for Visual Reasoning
with: Joulin, A.: effectiveness of MAE pre-pretraining for billion-scale pre...
with: Kannala, J.H.: Understanding Objects in Detail with Fine-Grained Attri...
with: Kar, A.: three R's of computer vision: Recognition, reconstruction and...
with: Kipman, A.: Efficient Human Pose Estimation from Single Depth Images
with: Kirillov, A.: Boundary IoU: Improving Object-Centric Image Segmentatio...
with: Kirillov, A.: Exploring Randomly Wired Neural Networks for Image Recog...
with: Kirillov, A.: Panoptic Feature Pyramid Networks
with: Kirillov, A.: Panoptic Segmentation
with: Kirillov, A.: PointRend: Image Segmentation As Rendering
with: Kirillov, A.: Segment Anything
with: Kohli, P.: Efficient Human Pose Estimation from Single Depth Images
with: Kohli, P.: Efficient Regression of General-Activity Human Poses from D...
with: Kokkinos, I.: Editorial: Deep Learning for Computer Vision
with: Kokkinos, I.: Understanding Objects in Detail with Fine-Grained Attrib...
with: Kosaraju, R.P.: Designing Network Design Spaces
with: Kosaraju, R.P.: Revisiting Weakly Supervised Pre-Training of Visual Pe...
with: Krahenbuhl, P.: Long-Term Feature Banks for Detailed Video Understanding
with: Krahenbuhl, P.: Multigrid Method for Efficiently Training Video Models...
with: Laptev, I.: Editorial: Deep Learning for Computer Vision
with: Li, Y.: Masked Autoencoders Are Scalable Vision Learners
with: Li, Y.H.: Exploring Plain Vision Transformer Backbones for Object Dete...
with: Li, Y.X.: Exploring the Limits of Weakly Supervised Pretraining
with: Lin, T.Y.: Feature Pyramid Networks for Object Detection
with: Lin, T.Y.: Focal Loss for Dense Object Detection
with: Liu, C.X.: Are Labels Necessary for Neural Architecture Search?
with: Lo, W.Y.: Segment Anything
with: Mahajan, D.: Exploring the Limits of Weakly Supervised Pretraining
with: Mahajan, D.: Revisiting Weakly Supervised Pre-Training of Visual Perce...
with: Mahendran, S.: Understanding Objects in Detail with Fine-Grained Attri...
with: Maji, S.: Understanding Objects in Detail with Fine-Grained Attributes
with: Malik, J.: Actions and Attributes from Wholes and Parts
with: Malik, J.: Aligning 3D models to RGB-D images of cluttered scenes
with: Malik, J.: Analyzing the Performance of Multilayer Neural Networks for...
with: Malik, J.: Contextual Action Recognition with R*CNN
with: Malik, J.: Deformable part models are convolutional neural networks
with: Malik, J.: Editorial: Deep Learning for Computer Vision
with: Malik, J.: Hypercolumns for object segmentation and fine-grained local...
with: Malik, J.: Indoor Scene Understanding with RGB-D Images: Bottom-up Seg...
with: Malik, J.: Learning Rich Features from RGB-D Images for Object Detecti...
with: Malik, J.: Object Instance Segmentation and Fine-Grained Localization ...
with: Malik, J.: Perceptual Organization and Recognition of Indoor Scenes fr...
with: Malik, J.: Region-Based Convolutional Networks for Accurate Object Det...
with: Malik, J.: Rich Feature Hierarchies for Accurate Object Detection and ...
with: Malik, J.: Simultaneous Detection and Segmentation
with: Malik, J.: three R's of computer vision: Recognition, reconstruction a...
with: Malik, J.: Training Deformable Part Models with Decorrelated Features
with: Malik, J.: Using k-Poselets for Detecting People and Localizing Their ...
with: Mao, H.Z.: Exploring Plain Vision Transformer Backbones for Object Det...
with: Mao, H.Z.: Segment Anything
with: Mintun, E.: Segment Anything
with: Misra, I.: effectiveness of MAE pre-pretraining for billion-scale pret...
with: Misra, I.: Learning by Asking Questions
with: Misra, I.: Seeing through the Human Reporting Bias: Visual Classifiers...
with: Mitchell, M.: Seeing through the Human Reporting Bias: Visual Classifi...
with: Mohamed, S.: Understanding Objects in Detail with Fine-Grained Attribu...
with: Moore, R.: Efficient Human Pose Estimation from Single Depth Images
with: Paluri, M.: Exploring the Limits of Weakly Supervised Pretraining
with: Papandreou, G.: Editorial: Deep Learning for Computer Vision
with: Pathak, D.: Learning Features by Watching Objects Move
with: Radosavovic, I.: Data Distillation: Towards Omni-Supervised Learning
with: Radosavovic, I.: Designing Network Design Spaces
with: Rahtu, E.: Understanding Objects in Detail with Fine-Grained Attributes
with: Ramanathan, V.: Exploring the Limits of Weakly Supervised Pretraining
with: Ravi, N.: Segment Anything
with: Redmon, J.: You Only Look Once: Unified, Real-Time Object Detection
with: Ren, S.Q.: Faster R-CNN: Towards Real-Time Object Detection with Regio...
with: Ren, S.Q.: Object Detection Networks on Convolutional Feature Maps
with: Rolland, C.: Segment Anything
with: Rother, C.: Panoptic Segmentation
with: Saphra, N.: Understanding Objects in Detail with Fine-Grained Attributes
with: Sharp, T.: Efficient Human Pose Estimation from Single Depth Images
with: Shotton, J.D.J.: Efficient Regression of General-Activity Human Poses ...
with: Shotton, J.J.D.: Efficient Human Pose Estimation from Single Depth Ima...
with: Shrivastava, A.: Training Region-Based Object Detectors with Online Ha...
with: Simonyan, K.: Understanding Objects in Detail with Fine-Grained Attrib...
with: Singh, M.: effectiveness of MAE pre-pretraining for billion-scale pret...
with: Singh, M.: Fast and Accurate Model Scaling
with: Singh, M.: Revisiting Weakly Supervised Pre-Training of Visual Percept...
with: Song, H.O.: Generalized Sparselet Models for Real-Time Multiclass Obje...
with: Song, H.O.: Sparselet Models for Efficient Multiclass Object Detection
with: Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region ...
with: Sun, J.: Object Detection Networks on Convolutional Feature Maps
with: Taskar, B.: Understanding Objects in Detail with Fine-Grained Attributes
with: Tsogkas, S.: Understanding Objects in Detail with Fine-Grained Attribu...
with: Tu, Z.: Aggregated Residual Transformations for Deep Neural Networks
with: Tulsiani, S.: three R's of computer vision: Recognition, reconstructio...
with: van der Maaten, L.: CLEVR: A Diagnostic Dataset for Compositional Lang...
with: van der Maaten, L.: Exploring the Limits of Weakly Supervised Pretrain...
with: van der Maaten, L.: Inferring and Executing Programs for Visual Reason...
with: van der Maaten, L.: Learning by Asking Questions
with: van der Maaten, L.: Revisiting Weakly Supervised Pre-Training of Visua...
with: Vedaldi, A.: Editorial: Deep Learning for Computer Vision
with: Vedaldi, A.: Understanding Objects in Detail with Fine-Grained Attribu...
with: Wang, X.G.: Editorial: Deep Learning for Computer Vision
with: Wang, X.L.: Non-local Neural Networks
with: Wang, Y.: Low-Shot Learning from Imaginary Data
with: Weiss, D.: Understanding Objects in Detail with Fine-Grained Attributes
with: Whitehead, S.: Segment Anything
with: Wu, C.Y.: Long-Term Feature Banks for Detailed Video Understanding
with: Wu, C.Y.: Multigrid Method for Efficiently Training Video Models, A
with: Wu, Y.: PointRend: Image Segmentation As Rendering
with: Wu, Y.X.: Momentum Contrast for Unsupervised Visual Representation Lea...
with: Xiao, T.: Segment Anything
with: Xie, J.Y.: Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep...
with: Xie, S.: Aggregated Residual Transformations for Deep Neural Networks
with: Xie, S.: Exploring Randomly Wired Neural Networks for Image Recognition
with: Xie, S.: Masked Autoencoders Are Scalable Vision Learners
with: Xie, S.N.: Are Labels Necessary for Neural Architecture Search?
with: Xie, S.N.: Momentum Contrast for Unsupervised Visual Representation Le...
with: Xiong, B.: Large-Scale Study on Unsupervised Spatiotemporal Representa...
with: Yan, S.C.: Editorial: Deep Learning for Computer Vision
with: Yuille, A.L.: Are Labels Necessary for Neural Architecture Search?
with: Yuille, A.L.: Editorial: Deep Learning for Computer Vision
with: Zhang, N.: Part-Based R-CNNs for Fine-Grained Category Detection
with: Zhang, X.: Object Detection Networks on Convolutional Feature Maps
with: Zickler, S.: Generalized Sparselet Models for Real-Time Multiclass Obj...
with: Zickler, S.: Sparselet Models for Efficient Multiclass Object Detection
with: Zitnick, C.L.: CLEVR: A Diagnostic Dataset for Compositional Language ...
with: Zitnick, C.L.: Inferring and Executing Programs for Visual Reasoning
with: Zitnick, C.L.: Inside-Outside Net: Detecting Objects in Context with S...
with: Zitnick, C.L.: Seeing through the Human Reporting Bias: Visual Classif...
279 for Girshick, R.
Girshick, R.B.
Standard Author Listing
with: Basri, R.: Visibility constraints on features of 3D objects
with: Felzenszwalb, P.F.: Cascade object detection with deformable part models
with: Felzenszwalb, P.F.: discriminatively trained, multiscale, deformable p...
with: Felzenszwalb, P.F.: Object Detection with Discriminatively Trained Par...
with: Felzenszwalb, P.F.: Visibility constraints on features of 3D objects
with: Felzenszwalb, P.F.: Visual Object Detection with Deformable Part Models
with: Jacobs, D.W.: Visibility constraints on features of 3D objects
with: Klivans, C.J.: Visibility constraints on features of 3D objects
with: McAllester, D.: Cascade object detection with deformable part models
with: McAllester, D.: discriminatively trained, multiscale, deformable part ...
with: McAllester, D.: Object Detection with Discriminatively Trained Part-Ba...
with: McAllester, D.: Visual Object Detection with Deformable Part Models
with: Ramanan, D.: Cascade object detection with deformable part models
with: Ramanan, D.: discriminatively trained, multiscale, deformable part mod...
with: Ramanan, D.: Object Detection with Discriminatively Trained Part-Based...
with: Ramanan, D.: Visual Object Detection with Deformable Part Models
16 for Girshick, R.B.