| Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture | 0 | 0.34 | 2022 |
| Webly Supervised Concept Expansion for General Purpose Vision Models. | 0 | 0.34 | 2022 |
| Learning Curves For Analysis Of Deep Networks | 0 | 0.34 | 2021 |
| Editorial: Introduction to the Special Section on CVPR2019 Best Papers | 0 | 0.34 | 2021 |
| Manhattan Room Layout Reconstruction From A Single 360 Degrees Image: A Comparative Study Of State-Of-The-Art Methods | 1 | 0.35 | 2021 |
| PatchMatch-RL - Deep MVS with Pixelwise Depth, Normal, and Visibility. | 0 | 0.34 | 2021 |
| Task-Assisted Domain Adaptation With Anchor Tasks | 0 | 0.34 | 2021 |
| Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion | 1 | 0.34 | 2020 |
| Improving Structure from Motion with Reliable Resectioning | 0 | 0.34 | 2020 |
| Improving Confidence Estimates For Unfamiliar Examples | 1 | 0.40 | 2020 |
| Silhouette Guided Point Cloud Reconstruction beyond Occlusion | 0 | 0.34 | 2020 |
| Complete 3D Scene Parsing from an RGBD Image | 1 | 0.34 | 2019 |
| No-Frills Human-Object Interaction Detection: Factorization, Appearance and Layout Encodings, and Training Techniques. | 0 | 0.34 | 2018 |
| FEATS: Synthetic Feature Tracks for Structure from Motion Evaluation | 0 | 0.34 | 2018 |
| G-Distillation: Reducing Overconfident Errors on Novel Samples. | 0 | 0.34 | 2018 |
| Complete 3D Scene Parsing from Single RGBD Image. | 0 | 0.34 | 2017 |
| Learning without Forgetting. | 161 | 5.17 | 2016 |
| Swapout: Learning an ensemble of deep architectures. | 18 | 0.98 | 2016 |
| Geometry-Informed Material Recognition | 4 | 0.41 | 2016 |
| 3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera. | 1 | 0.36 | 2016 |
| Learning to Localize Little Landmarks | 0 | 0.34 | 2016 |
| Predicting Complete 3D Models of Indoor Scenes. | 9 | 0.56 | 2015 |
| Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization | 10 | 0.50 | 2015 |
| Learning Discriminative Collections of Part Detectors for Object Recognition | 4 | 0.39 | 2015 |
| Guest Editorial: Scene Understanding | 3 | 0.70 | 2015 |
| Family Member Identification from Photo Collections | 2 | 0.39 | 2015 |
| Where To Look: Focus Regions For Visual Question Answering | 87 | 2.28 | 2015 |
| Labeling Complete Surfaces in Scene Understanding | 4 | 0.41 | 2015 |
| Category-independent object proposals with diverse ranking. | 87 | 3.84 | 2014 |
| Support Surface Prediction in Indoor Scenes | 28 | 1.21 | 2013 |
| Learning Collections of Part Models for Object Recognition | 46 | 1.72 | 2013 |
| Boundary Cues for 3D Object Shape Recovery | 11 | 0.59 | 2013 |
| Paired regions for shadow detection and removal. | 52 | 1.39 | 2013 |
| Improved object categorization and detection using comparative object similarity. | 11 | 0.51 | 2013 |
| Diagnosing error in object detectors | 86 | 15.09 | 2012 |
| Learning image similarity from Flickr groups using fast kernel machines. | 24 | 0.82 | 2012 |
| Learning to localize detected objects | 1 | 0.35 | 2012 |
| Learning shared body plans | 0 | 0.34 | 2012 |
| A data driven method for feature transformation | 13 | 5.65 | 2012 |
| Beyond the line of sight: labeling the underlying surfaces | 26 | 1.09 | 2012 |
| Recovering free space of indoor scenes from a single image | 50 | 2.07 | 2012 |
| Indoor segmentation and support inference from RGBD images | 806 | 25.94 | 2012 |
| Recovering Occlusion Boundaries from an Image | 84 | 5.94 | 2011 |
| Rendering synthetic objects into legacy photographs | 78 | 2.38 | 2011 |
| Single-image shadow detection and removal using paired regions | 75 | 2.29 | 2011 |
| Comparative object similarity for improved recognition with few or no examples | 33 | 1.36 | 2010 |
| Attribute-centric recognition for cross-category generalization | 109 | 5.22 | 2010 |
| Category Independent Object Proposals | 67 | 10.59 | 2010 |
| It's All About the Data | 6 | 0.49 | 2010 |
| Thinking inside the box: using appearance models and context based on room geometry | 123 | 6.45 | 2010 |
