  In this paper, we point out that the essential differences between CNN-based
and Transformer-based detectors, which cause the worse performance of small
objects 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 pre-experiments that indicate the gap causes
CNN-based and Transformer-based methods to increase size-different objects
result unevenly. Different from the divide and conquer strategy in previous
methods, Hyneters consist of Hybrid Network Backbone (HNB) and Dual Switching
module (DS), 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, and DS adjusts excessive reliance on global dependencies outside the
patch.
