CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection

2026-08-27

AI Summary

为解决开放世界物体检测中的语义模糊和未知物体过度抑制问题，提出CODE框架，通过跨模态校准、不确定性引导增强及动态抑制方法提升检测性能。

Abstract

Open World Object Detection (OWOD) built on multimodal foundation models often suffers from semantic ambiguity caused by unidirectional text-to-vision matching, while rigid outlier penalties may over-suppress unknown objects near known-class decision boundaries. We propose CODE (Cross-Modal Calibration and Dynamic Suppression), a unified inference-time framework with three complementary components. Cross-Modal Joint Confidence Calibration injects global visual prototypes to calibrate text-driven known-class predictions. Uncertainty-Guided Universal Objectness Enhancement measures classification hesitation from local visual responses to strengthen potential unknown objects. Dynamic Outlier Suppression via Confidence Margin replaces rigid suppression with a margin-aware adjustment that preserves ambiguous out-of-distribution instances. Experiments on the Real-World Detection benchmark demonstrate that, with the OWL-ViT L/14 backbone, CODE achieves 21.7 U-mAP and 40.8 K-mAP in Task 1, surpassing the previous state of the art by 2.6 and 2.3 points, respectively.

Problem

• Open World Object Detection
• semantic ambiguity
• unidirectional text-to-vision matching
• outlier penalties
• unknown objects

Innovation

• Cross-Modal Calibration
• Dynamic Suppression
• Open World Object Detection
• Uncertainty-Guided Enhancement
• Confidence Margin