NoOVD：开放词汇物体检测中的新类别发现与嵌入
NoOVD: Novel Category Discovery and Embedding for Open-Vocabulary Object Detection
AI总结：
本文提出NoOVD框架，通过自蒸馏机制和K-FPN模块提升开放词汇物体检测中新类别的识别与嵌入效果，同时引入R-RPN提升召回率，实验表明在多个数据集上表现优异。
摘要：
尽管开放词汇物体检测（OVD）取得了显著进展，但在训练和测试阶段仍存在明显差距。训练阶段，RPN和RoI头常将未标记的新类别物体误分类为背景，导致部分提案被RPN提前过滤，其余被RoI头进一步误分类。测试阶段，这些提案再次获得低分并被后处理移除，导致召回率显著下降，最终削弱新类别检测性能。为解决这些问题，我们提出NoOVD新训练框架，创新性地整合基于冻结视觉-语言模型（VLMs）知识的自蒸馏机制。具体而言，我们设计K-FPN，利用预训练的VLMs知识引导模型发现新类别物体并促进知识蒸馏，无需额外数据，从而防止强制将新物体与背景对齐。此外，我们引入R-RPN，在推理阶段调整提案的置信度分数以提高新类别物体的召回率。跨数据集评估表明，我们的方法在OV-LVIS、OV-COCO和Objects365等数据集上均在多个指标上取得优越性能。
英文摘要：
Despite the remarkable progress in open-vocabulary object detection (OVD), a significant gap remains between the training and testing phases. During training, the RPN and RoI heads often misclassify unlabeled novel-category objects as background, causing some proposals to be prematurely filtered out by the RPN while others are further misclassified by the RoI head. During testing, these proposals again receive low scores and are removed in post-processing, leading to a significant drop in recall and ultimately weakening novel-category detection performance.To address these issues, we propose a novel training framework-NoOVD-which innovatively integrates a self-distillation mechanism grounded in the knowledge of frozen vision-language models (VLMs). Specifically, we design K-FPN, which leverages the pretrained knowledge of VLMs to guide the model in discovering novel-category objects and facilitates knowledge distillation-without requiring additional data-thus preventing forced alignment of novel objects with background.Additionally, we introduce R-RPN, which adjusts the confidence scores of proposals during inference to improve the recall of novel-category objects. Cross-dataset evaluations on OV-LVIS, OV-COCO, and Objects365 demonstrate that our approach consistently achieves superior performance across multiple metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
