Girshick, R.[Ross]
* 2011: Efficient Regression of General-Activity Human Poses from Depth Images
* 2012: Sparselet Models for Efficient Multiclass Object Detection
* 2013: Efficient Human Pose Estimation from Single Depth Images
* 2013: Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images
* 2013: Training Deformable Part Models with Decorrelated Features
* 2014: Analyzing the Performance of Multilayer Neural Networks for Object Recognition
* 2014: Learning Rich Features from RGB-D Images for Object Detection and Segmentation
* 2014: Part-Based R-CNNs for Fine-Grained Category Detection
* 2014: Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
* 2014: Simultaneous Detection and Segmentation
* 2014: Understanding Objects in Detail with Fine-Grained Attributes
* 2014: Using k-Poselets for Detecting People and Localizing Their Keypoints
* 2015: Actions and Attributes from Wholes and Parts
* 2015: Aligning 3D models to RGB-D images of cluttered scenes
* 2015: Contextual Action Recognition with R*CNN
* 2015: Deformable part models are convolutional neural networks
* 2015: Fast R-CNN
* 2015: Generalized Sparselet Models for Real-Time Multiclass Object Recognition
* 2015: Hypercolumns for object segmentation and fine-grained localization
* 2015: Indoor Scene Understanding with RGB-D Images: Bottom-up Segmentation, Object Detection and Semantic Segmentation
* 2016: Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
* 2016: Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
* 2016: Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
* 2016: Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
* 2016: three R's of computer vision: Recognition, reconstruction and reorganization, The
* 2016: Training Region-Based Object Detectors with Online Hard Example Mining
* 2016: You Only Look Once: Unified, Real-Time Object Detection
* 2017: Aggregated Residual Transformations for Deep Neural Networks
* 2017: CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
* 2017: Editorial: Deep Learning for Computer Vision
* 2017: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
* 2017: Feature Pyramid Networks for Object Detection
* 2017: Focal Loss for Dense Object Detection
* 2017: Inferring and Executing Programs for Visual Reasoning
* 2017: Learning Features by Watching Objects Move
* 2017: Low-Shot Visual Recognition by Shrinking and Hallucinating Features
* 2017: Mask R-CNN
* 2017: Object Detection Networks on Convolutional Feature Maps
* 2017: Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns
* 2018: Data Distillation: Towards Omni-Supervised Learning
* 2018: Detecting and Recognizing Human-Object Interactions
* 2018: Exploring the Limits of Weakly Supervised Pretraining
* 2018: Learning by Asking Questions
* 2018: Learning to Segment Every Thing
* 2018: Low-Shot Learning from Imaginary Data
* 2018: Non-local Neural Networks
* 2019: Exploring Randomly Wired Neural Networks for Image Recognition
* 2019: Long-Term Feature Banks for Detailed Video Understanding
* 2019: LVIS: A Dataset for Large Vocabulary Instance Segmentation
* 2019: Panoptic Feature Pyramid Networks
* 2019: Panoptic Segmentation
* 2019: Rethinking ImageNet Pre-Training
* 2019: TensorMask: A Foundation for Dense Object Segmentation
* 2020: Are Labels Necessary for Neural Architecture Search?
* 2020: Designing Network Design Spaces
* 2020: Focal Loss for Dense Object Detection
* 2020: Mask R-CNN
* 2020: Momentum Contrast for Unsupervised Visual Representation Learning
* 2020: Multigrid Method for Efficiently Training Video Models, A
* 2020: PointRend: Image Segmentation As Rendering
* 2021: Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
* 2021: Fast and Accurate Model Scaling
* 2021: Large-Scale Study on Unsupervised Spatiotemporal Representation Learning, A
* 2022: Exploring Plain Vision Transformer Backbones for Object Detection
* 2022: Masked Autoencoders Are Scalable Vision Learners
* 2022: Revisiting Weakly Supervised Pre-Training of Visual Perception Models
* 2023: effectiveness of MAE pre-pretraining for billion-scale pretraining, The
* 2023: Segment Anything
Includes: Girshick, R.[Ross] Girshick, R.
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Girshick, R.B.[Ross B.]
* 2008: discriminatively trained, multiscale, deformable part model, A
* 2009: Visibility constraints on features of 3D objects
* 2010: Cascade object detection with deformable part models
* 2010: Object Detection with Discriminatively Trained Part-Based Models
* 2013: Visual Object Detection with Deformable Part Models