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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 perceptualcapabilities, such as deformation perception, global perception and cross-taskperception. Despite numerous methods attempt to enhance these abilities from asingle aspect, achieving a comprehensive and unified solution remains asignificant challenge. In response to this challenge, we have developed aninnovative detection head, termed UniHead, to unify three perceptual abilitiessimultaneously. More precisely, our approach (1) introduces deformationperception, enabling the model to adaptively sample object features; (2)proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-rangedependencies, thereby achieving global perception; and (3) devises a Cross-taskInteraction Transformer (CIT) that facilitates interaction between theclassification and localization branches, thus aligning the two tasks. As aplug-and-play method, the proposed UniHead can be conveniently integrated withexisting detectors. Extensive experiments on the COCO dataset demonstrate thatour 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.

UniHead: Unifying Multi-Perception for Detection Heads