  Cascade is a widely used approach that rejects obvious negative samples at
early stages for learning better classifier and faster inference. This paper
presents chained cascade network (CC-Net). In this CC-Net, the cascaded
classifier at a stage is aided by the classification scores in previous stages.
Feature chaining is further proposed so that the feature learning for the
current cascade stage uses the features in previous stages as the prior
information. The chained ConvNet features and classifiers of multiple stages
are jointly learned in an end-to-end network. In this way, features and
classifiers at latter stages handle more difficult samples with the help of
features and classifiers in previous stages. It yields consistent boost in
detection performance on benchmarks like PASCAL VOC 2007 and ImageNet. Combined
with better region proposal, CC-Net leads to state-of-the-art result of 81.1%
mAP on PASCAL VOC 2007.
