SaccadeNet: A Fast and Accurate Object Detector
Shiyi Lan,  Zhou Ren,  Yi Wu,  Larry S. Davis,  Gang Hua

Object detection is an essential step towards holistic scene understanding. Most existing object detection algorithms attend to certain object areas once and then predict the object locations. However, scientists have revealed that human do not look at the scene in fixed steadiness. Instead, human eyes move around, locating informative parts to understand the object location. This active perceiving movement process is called saccade. In this paper, inspired by such mechanism, we propose a fast and accurate object detector called SaccadeNet. It contains four main modules, the Center Attentive Module, the Corner Attentive Module, the Attention Transitive Module, and the Aggregation Attentive Module, which allows it to attend to different informative object keypoints actively, and predict object locations from coarse to fine. The Corner Attentive Module is used only during training to extract more informative corner features which brings free-lunch performance boost. On the MS COCO dataset, we achieve the performance of 40.4% mAP at 28 FPS and 30.5% mAP at 118 FPS. Among all the real-time object detectors, our SaccadeNet achieves the best detection performance, which demonstrates the effectiveness of the proposed detection mechanism.