TaskGuard：面向风险感知目标检测的任务条件化恢复效用
TaskGuard: Task-Conditioned Restoration Utility for Risk-Aware Object Detection
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中文总结 AI 辅助
TaskGuard通过预测恢复干预的任务效用，在未见退化上减少54.2%损失性干预并保留98.8%AP，证明恢复效用是任务条件化属性。
中文摘要 AI 辅助
在恶劣条件下进行目标检测前通常先应用图像恢复，然而视觉上改善的图像未必能提升下游任务性能。我们将这一不匹配问题作为恢复效用预测来研究：给定退化图像及其候选恢复结果，应使用该恢复结果还是保留原始观测？我们提出TaskGuard，一种用于冻结的恢复与检测流程的事后控制器。TaskGuard通过恢复残差与检测器敏感性的交互来刻画实际恢复残差，并预测该干预是否对任务有益。精确的区域反事实分析揭示了图像内部显著的效用异质性，而可部署的伪梯度保留了统计上可靠的方向性信息。特征组消融进一步表明，任务条件化证据提供了超越检测器响应和残差统计的额外信息。TaskGuard效用预测器仅在高斯退化上训练并在最终评估前冻结，然后迁移到未见过的运动模糊、雨和散焦退化。在这些未见过的退化族中，TaskGuard将损失性干预减少了54.2%（族宏平均），将实际每图像检测退化减少了37.0%（合并），同时保留了Always-Restore COCO AP的98.8%。在自然雨DAWN数据集上，它减少了97.9%的损失性干预，同时保留了去雨所获得AP提升的77.8%。这些结果支持恢复效用是特定干预的任务条件化属性，而非仅由图像外观决定。
英文摘要
Image restoration is commonly applied before object detection under adverse conditions, yet a visually improved image need not improve the downstream task. We study this mismatch as restoration utility prediction: given a degraded image and its candidate restoration, should the restoration be used or should the original observation be preserved? We introduce TaskGuard, a post-hoc controller for frozen restoration and detection pipelines. TaskGuard characterizes the realized restoration residual through its interaction with detector sensitivity and predicts whether the intervention is task-beneficial. Exact regional counterfactuals reveal substantial within-image utility heterogeneity, while a deployable pseudo-gradient preserves statistically reliable directional information. Feature-group ablation further shows that task-conditioned evidence contributes information beyond detector-response and residual statistics. The TaskGuard utility predictor is trained only on Gaussian degradation and frozen before final evaluation, then transferred to unseen motion blur, rain, and defocus. Across these unseen families, TaskGuard reduces lossnegative interventions by 54.2% (family macro) and practical per-image detection deteriorations by 37.0% (pooled), while preserving 98.8% of the Always-Restore COCO AP. On natural-rain DAWN, it reduces loss-negative interventions by 97.9% while retaining 77.8% of the AP improvement obtained by deraining. These results support restoration utility as a task-conditioned property of the specific intervention rather than image appearance alone.
发表机构
- Sam Houston State University（萨姆休斯顿州立大学）
机构由 AI 辅助整理，请以论文原文为准。
