  Finetuning from a pretrained deep model is found to yield state-of-the-art
performance for many vision tasks. This paper investigates many factors that
influence the performance in finetuning for object detection. There is a
long-tailed distribution of sample numbers for classes in object detection. Our
analysis and empirical results show that classes with more samples have higher
impact on the feature learning. And it is better to make the sample number more
uniform across classes. Generic object detection can be considered as multiple
equally important tasks. Detection of each class is a task. These classes/tasks
have their individuality in discriminative visual appearance representation.
Taking this individuality into account, we cluster objects into visually
similar class groups and learn deep representations for these groups
separately. A hierarchical feature learning scheme is proposed. In this scheme,
the knowledge from the group with large number of classes is transferred for
learning features in its sub-groups. Finetuned on the GoogLeNet model,
experimental results show 4.7% absolute mAP improvement of our approach on the
ImageNet object detection dataset without increasing much computational cost at
the testing stage.
