| Data Augmentation for Intent Classification with Off-the-shelf Large Language Models | 0 | 0.34 | 2022 |
| Multi-label Iterated Learning for Image Classification with Label
Ambiguity | 0 | 0.34 | 2022 |
| CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions | 0 | 0.34 | 2022 |
| Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. | 0 | 0.34 | 2021 |
| 3D Perception With Slanted Stixels on GPU | 0 | 0.34 | 2021 |
| Learning Data Augmentation With Online Bilevel Optimization For Image Classification | 0 | 0.34 | 2021 |
| SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction | 0 | 0.34 | 2021 |
| Synbols: Probing Learning Algorithms with Synthetic Datasets | 0 | 0.34 | 2020 |
| Looc: Localize Overlapping Objects With Count Supervision | 0 | 0.34 | 2020 |
| Knowledge Hypergraphs: Prediction Beyond Binary Relations | 1 | 0.35 | 2020 |
| Pix2Shape -- Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation | 2 | 0.41 | 2020 |
| Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning | 1 | 0.34 | 2020 |
| Proposal-Based Instance Segmentation With Point Supervision | 0 | 0.34 | 2020 |
| Context-Aware Visual Compatibility Prediction | 3 | 0.37 | 2019 |
| Slanted Stixels: A Way to Represent Steep Streets | 0 | 0.34 | 2019 |
| The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. | 25 | 0.72 | 2017 |
| GPU-accelerated real-time stixel computation. | 0 | 0.34 | 2017 |
| On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts. | 16 | 0.76 | 2017 |
| On-Board Detection of Pedestrian Intentions. | 6 | 0.55 | 2017 |
| Training my car to see using virtual worlds. | 0 | 0.34 | 2017 |
| Comparison of two non-linear model-based control strategies for autonomous vehicles. | 0 | 0.34 | 2017 |
| Slanted Stixels: Representing San Francisco's Steepest Streets. | 2 | 0.37 | 2017 |
| Gpu-Based Pedestrian Detection For Autonomous Driving | 8 | 0.62 | 2016 |
| Node-Adapt, Path-Adapt and Tree-Adapt: Model-Transfer Domain Adaptation for Random Forest. | 0 | 0.34 | 2016 |
| A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images. | 12 | 0.53 | 2016 |
| Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison. | 27 | 1.11 | 2016 |
| PixelVAE: A Latent Variable Model for Natural Images. | 3 | 0.36 | 2016 |
| The Synthia Dataset: A Large Collection Of Synthetic Images For Semantic Segmentation Of Urban Scenes | 77 | 1.70 | 2016 |
| 3d-Guided Multiscale Sliding Window For Pedestrian Detection | 2 | 0.42 | 2015 |
| Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection | 16 | 0.95 | 2015 |
| Vision-Based Offline-Online Perception Paradigm for Autonomous Driving | 27 | 1.22 | 2015 |
| Virtual and Real World Adaptation for Pedestrian Detection. | 53 | 1.96 | 2014 |
| Hierarchical Adaptive Structural SVM for Domain Adaptation. | 9 | 0.66 | 2014 |
| Spatiotemporal Stacked Sequential Learning For Pedestrian Detection | 3 | 0.39 | 2014 |
| Occlusion Handling via Random Subspace Classifiers for Human Detection | 8 | 0.45 | 2014 |
| Domain Adaptation of Deformable Part-Based Models | 32 | 0.86 | 2014 |
| Incremental Domain Adaptation of Deformable Part-based Models. | 6 | 0.41 | 2014 |
| Cost-Sensitive Structured SVM for Multi-category Domain Adaptation | 1 | 0.35 | 2014 |
| Learning a Part-Based Pedestrian Detector in a Virtual World | 7 | 0.51 | 2014 |
| Learning a multiview part-based model in virtual world for pedestrian detection | 2 | 0.37 | 2013 |
| Random Forests of Local Experts for Pedestrian Detection | 45 | 1.24 | 2013 |
| Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes | 0 | 0.34 | 2013 |
| Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers | 6 | 0.50 | 2013 |
| Unsupervised domain adaptation of virtual and real worlds for pedestrian detection | 11 | 0.54 | 2012 |
| Color contribution to part-based person detection in different types of scenarios | 4 | 0.41 | 2011 |
| Opponent colors for human detection | 3 | 0.39 | 2011 |
| Virtual worlds and active learning for human detection | 6 | 0.49 | 2011 |
| Learning Appearance In Virtual Scenarios For Pedestrian Detection | 64 | 2.30 | 2010 |
