UniHead: Unifying Multi-Perception for Detection Heads
Hantao Zhou Rui Yang Yachao Zhang Haoran Duan Yawen Huang Runze Hu Xiu Li Yefeng Zheng
The detection head constitutes a pivotal component within object detectors,
tasked with executing both classification and localization functions.
Regrettably, the commonly used parallel head often lacks omni perceptual
capabilities, such as deformation perception, global perception and cross-task
perception. Despite numerous methods attempt to enhance these abilities from a
single aspect, achieving a comprehensive and unified solution remains a
significant challenge. In response to this challenge, we have developed an
innovative detection head, termed UniHead, to unify three perceptual abilities
simultaneously. More precisely, our approach (1) introduces deformation
perception, enabling the model to adaptively sample object features; (2)
proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range
dependencies, thereby achieving global perception; and (3) devises a Cross-task
Interaction Transformer (CIT) that facilitates interaction between the
classification and localization branches, thus aligning the two tasks. As a
plug-and-play method, the proposed UniHead can be conveniently integrated with
existing detectors. Extensive experiments on the COCO dataset demonstrate that
our UniHead can bring significant improvements to many detectors. For instance,
the UniHead can obtain +2.7 AP gains in RetinaNet, +2.9 AP gains in FreeAnchor,
and +2.1 AP gains in GFL. The code will be publicly available. Code Url:
https://github.com/zht8506/UniHead.