  The Imagenet Large Scale Visual Recognition Challenge (ILSVRC) is the one of
the most important big data challenges to date. We participated in the object
detection track of ILSVRC 2014 and received the fourth place among the 38
teams. We introduce in our object detection system a number of novel techniques
in localization and recognition. For localization, initial candidate proposals
are generated using selective search, and a novel bounding boxes regression
method is used for better object localization. For recognition, to represent a
candidate proposal, we adopt three features, namely, RCNN feature, IFV feature,
and DPM feature. Given these features, category-specific combination functions
are learned to improve the object recognition rate. In addition, object context
in the form of background priors and object interaction priors are learned and
applied in our system. Our ILSVRC 2014 results are reported alongside with the
results of other participating teams.
