Convolutional Channel Features
arXiv:1504.07339
Abstract
Deep learning methods are powerful tools but often suffer from expensive computation and limited flexibility. An alternative is to combine light-weight models with deep representations. As successful cases exist in several visual problems, a unified framework is absent. In this paper, we revisit two widely used approaches in computer vision, namely filtered channel features and Convolutional Neural Networks (CNN), and absorb merits from both by proposing an integrated method called Convolutional Channel Features (CCF). CCF transfers low-level features from pre-trained CNN models to feed the boosting forest model. With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods. We show that CCF serves as a good way of tailoring pre-trained CNN models to diverse tasks without fine-tuning the whole network to each task by achieving state-of-the-art performances in pedestrian detection, face detection, edge detection and object proposal generation.
9 pages, 5 figures, 6 tables; ICCV 2015 camera-ready version
References in corpus (15)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Going Deeper with Convolutions
- Fully Convolutional Networks for Semantic Segmentation
- DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
- R-CNNs for Pose Estimation and Action Detection
- Ten Years of Pedestrian Detection, What Have We Learned?
- Deformable Part Models are Convolutional Neural Networks
- Filtered Channel Features for Pedestrian Detection
- Taking a Deeper Look at Pedestrians
- Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection
- Boosting Convolutional Features for Robust Object Proposals
- DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection
- Generic Object Detection With Dense Neural Patterns and Regionlets