TFPN: Twin Feature Pyramid Networks for Object Detection FPN (Feature Pyramid Networks) is one of the most popular object detection networks, which can improve small object detection by enhancing shallow features. However, limited attention has been paid to the improvement of large object detection via deeper feature enhancement. One existing approach merges the feature maps of different layers into a new feature map for object detection, but can lead to increased noise and loss of information. The other approach adds a bottomup structure after the feature pyramid of FPN, which superimposes the information from shallow layers into the deep feature map but weakens the strength of FPN in detecting small objects. To address these challenges, this paper proposes TFPN (Twin Feature Pyramid Networks), which consists of (1) FPN, a bottomup structure that improves large object detection; (2) TPS, a Twin Pyramid Structure that improves medium object detection; and (3) innovative integration of these two with FPN, which can significantly improve the detection accuracy of large and medium objects while maintaining the advantage of FPN in small object detection. Extensive experiments using the MSCOCO object detection datasets and the BDD100K automatic driving dataset demonstrate that TFPN significantly improves over existing models, achieving up to 2.2 improvement in detection accuracy (e.g., 36.3 for FPN vs. 38.5 for TFPN on COCO Val17). Our method can obtain the same accuracy as FPN with ResNet101 based on ResNet50 and needs fewer parameters.

Salient Object Detection by Pyramid Networks with Gating In the paper, we propose a salient object detection architecture named as Gating for Double Pyramid Networks (GDPNet). It consists of two pyramid structures: Feature Pyramid Network (FPN) and Pyramid Pooling Module (PPM). FPN has the capability of capturing the inherent multiscale and pyramid hierarchy, while PPM can exploit the global context information by differentregionbased context aggregation. It is known that the irrelevant information corresponding to nonsalient objects or background may deteriorate the performance of the model. We introduce two gating strategies, i.e., CrossGating for FPN and SingleGating for PPM, to suppress the incurred irrelevant information in hidden features. Our proposed approach achieves stateoftheart performance on five benchmark datasets, which demonstrates the effectiveness and robust feature extraction capability of proposed GDPNet.