In this article, we point out that the essential differences between convolutional neural network (CNN)-based and transformer-based detectors, which cause worse performance of small object in transformer-based methods, are the gap between local information and global dependencies in feature extraction and propagation. To address these differences, we propose a new vision transformer, called Hybrid Network Transformer (Hyneter), after preexperiments that indicate the gap causes CNN-based and transformer-based methods to increase size-different objects results unevenly. Different from the divide-and-conquer strategy in previous methods, Hyneters consist of hybrid network backbone (HNB) and dual switching (DS) module, which integrate local information and global dependencies, and transfer them simultaneously. Based on the balance strategy, HNB extends the range of local information by embedding convolution layers into transformer blocks in parallel, and DS adjusts excessive reliance on global dependencies outside the patch. Ablation studies illustrate that Hyneters achieve the state-of-the-art performance by a large margin of +2.1 similar to 13.2AP on COCO, and +3.1 similar to 6.5mIoU on VisDrone with lighter model size and lower computational cost in object detection. Furthermore, Hyneters achieve the state-of-the-art results on multiple computer vision tasks, such as object detection ( 60.1AP on COCO and 46.1AP on VisDrone), semantic segmentation ( 54.3AP on ADE20K), and instance segmentation ( 48.5AP(mask) on COCO), and surpass previous best methods. The code will be publicly available later.
