Computer Vision Reading Group

People in object detection

Kaiming He

arxiv paper list :white_check_mark:

1. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition paper code
2. Convolutional Neural Networks at Constrained Time Cost paper
3. Efficient and Accurate Approximations of Nonlinear Convolutional Networks paper
4. ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation paper
5. Instance-aware Semantic Segmentation via Multi-task Network Cascades paper
6. Deep Residual Learning for Image Recognition paper
7. Identity Mappings in Deep Residual Networks paper
8. Instance-sensitive Fully Convolutional Networks paper
9. Is Faster R-CNN Doing Well for Pedestrian Detection? paper
10. R-FCN: Object Detection via Region-based Fully Convolutional Networks paper
11. Aggregated Residual Transformations for Deep Neural Networks paper
12. Feature Pyramid Networks for Object Detection paper
13. Mask R-CNN paper
14. Detecting and Recognizing Human-Object Interactions paper

Ross Girshick

arxiv paper list :white_check_mark:

Trevor Darrell

arxiv paper list :white_check_mark:

Rogerio Feris

1. S3Pool: Pooling with Stochastic Spatial Sampling paper code
2. Deep Domain Adaptation for Describing People Based on Fine-Grained Clothing Attributes paper
3. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection paper caffe
4. Shape Classification Through Structured Learning of Matching Measures paper
5. Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance paper
6. Boosting Object Detection Performance in Crowded Surveillance Videos paper
7. Efficient Maximum Appearance Search for Large-Scale Object Detection paper
8. Fast Face Detector Training Using Tailored Views paper
9. Attribute-based People Search: Lessons Learnt from a Practical Surveillance System paper
10. Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification paper
11. [BOOK] Visual Attributes link introduce

Piotr Dollar

blog

1. Supervised Learning of Edges and Object Boundaries paper
2. Multiple Component Learning for Object Detection paper
3. Fast Feature Pyramids for Object Detection paper
4. Detecting Objects using Deformation Dictionaries paper
5. Edge Boxes: Locating Object Proposals from Edges paper
6. What makes for effective detection proposals? paper
7. Learning to Segment Object Candidates paper
8. Semantic Amodal Segmentation paper
9. Unsupervised Learning of Edges paper
10. A MultiPath Network for Object Detection paper
11. Learning to Refine Object Segments paper

Xiaoyu Wang

1. Regionlets for Generic Object Detection ICCV 2013 T-PAMI 2015
2. Generic Object Detection with Dense Neural Patterns and Regionlets paper
3. Accurate Object Detection with Location Relaxation and Regionlets Relocalization paper
4. Deep Reinforcement Learning-based Image Captioning with Embedding Reward paper
5. SEP-Nets: Small and Effective Pattern Networks paper

Rodrigo Benenson

1. Traffic Sign Recognition – How far are we from the solution paper
2. Seeking the strongest rigid detector paper
3. How good are detection proposals, really? paper
4. Ten Years of Pedestrian Detection, What Have We Learned? paper
5. Taking a Deeper Look at Pedestrians paper
6. Filtered Channel Features for Pedestrian Detection paper
7. What makes for effective detection proposals? paper
8. What is Holding Back Convnets for Detection? paper
9. Weakly Supervised Object Boundaries paper
10. How Far are We from Solving Pedestrian Detection? paper
11. The Cityscapes Dataset paper
12. Detecting Surgical Tools by Modelling Local Appearance and Global Shape paper

Jan Hosang

1. A convnet for non-maximum suppression paper
2. Simple does it: Weakly supervised instance and semantic segmentation paper
3. Learning non-maximum suppression paper

Workshop

1. ICCV 2015 Tutorial on Tools for Efficient Object Detection