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

Learning Chained Deep Features and Classifiers for Cascade in Object Detection