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