  Building on the success of recent discriminative mid-level elements, we
propose a surprisingly simple approach for object detection which performs
comparable to the current state-of-the-art approaches on PASCAL VOC comp-3
detection challenge (no external data). Through extensive experiments and
ablation analysis, we show how our approach effectively improves upon the
HOG-based pipelines by adding an intermediate mid-level representation for the
task of object detection. This representation is easily interpretable and
allows us to visualize what our object detector "sees". We also discuss the
insights our approach shares with CNN-based methods, such as sharing
representation between categories helps.
