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:
this https URL

我们提出了一种名为 UniHead 的创新检测头，同时统一了三种感知能力：变形感知、全局感知和跨任务交互，使得我们的方法在 COCO 数据集上显著改进了多个检测器的性能。

UniHead：统一多感知检测算法

UniHead: Unifying Multi-Perception for Detection Heads

Domain adaptive object detection (DAOD) aims to generalize detectors trained
on an annotated source domain to an unlabelled target domain. However, existing
methods focus on reducing the domain bias of the detection backbone by
inferring a discriminative visual encoder, while ignoring the domain bias in
the detection head. Inspired by the high generalization of vision-language
models (VLMs), applying a VLM as the robust detection backbone following a
domain-aware detection head is a reasonable way to learn the discriminative
detector for each domain, rather than reducing the domain bias in traditional
methods. To achieve the above issue, we thus propose a novel DAOD framework
named Domain-Aware detection head with Prompt tuning (DA-Pro), which applies
the learnable domain-adaptive prompt to generate the dynamic detection head for
each domain. Formally, the domain-adaptive prompt consists of the
domain-invariant tokens, domain-specific tokens, and the domain-related textual
description along with the class label. Furthermore, two constraints between
the source and target domains are applied to ensure that the domain-adaptive
prompt can capture the domains-shared and domain-specific knowledge. A prompt
ensemble strategy is also proposed to reduce the effect of prompt disturbance.
Comprehensive experiments over multiple cross-domain adaptation tasks
demonstrate that using the domain-adaptive prompt can produce an effectively
domain-related detection head for boosting domain-adaptive object detection.

提出了一种名为 DA-Pro 的新型 DAOD 框架，将可学习的域适应提示应用于生成每个域的动态检测头，以从每个域中学习辨别探测器，以提高域自适应目标检测效果。