  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.
