In the field of Class Incremental Object Detection (CIOD), creating models
that can continuously learn like humans is a major challenge. Pseudo-labeling
methods, although initially powerful, struggle with multi-scenario incremental
learning due to their tendency to forget past knowledge. To overcome this, we
introduce a new approach called Vision-Language Model assisted Pseudo-Labeling
(VLM-PL). This technique uses Vision-Language Model (VLM) to verify the
correctness of pseudo ground-truths (GTs) without requiring additional model
training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector.
Then, we generate custom queries for each pseudo GT using carefully designed
prompt templates that combine image and text features. This allows the VLM to
classify the correctness through its responses. Furthermore, VLM-PL integrates
refined pseudo and real GTs from upcoming training, effectively combining new
and old knowledge. Extensive experiments conducted on the Pascal VOC and MS
COCO datasets not only highlight VLM-PL's exceptional performance in
multi-scenario but also illuminate its effectiveness in dual-scenario by
achieving state-of-the-art results in both.

在 Class Incremental Object Detection（CIOD）领域，解决模型如何像人类一样持续学习的问题是一个重大挑战。为了克服传统的伪标签方法在多场景增量学习中遗忘过去知识的问题，该研究介绍了一种名为 Vision-Language Model assisted Pseudo-Labeling（VLM-PL）的新方法。通过将图像和文本特征组合设计的提示模板生成自定义查询，借助视觉 - 语言模型（VLM），VLM-PL 技术能够验证伪标签的正确性，而无需额外的模型训练。通过将精细化的伪标签和真实标签整合，并结合新旧知识，VLM-PL 在 Pascal VOC 和 MS COCO 数据集上进行了广泛实验，不仅在多场景问题上表现出色，还在双场景问题上取得了最先进的结果。

VLM-PL：基于视觉 - 语言模型的高级伪标签方法的类别增量对象检测

VLM-PL: Advanced Pseudo Labeling approach Class Incremental Object  Detection with Vision-Language Model

In the field of class incremental learning (CIL), genera- tive replay has
become increasingly prominent as a method to mitigate the catastrophic
forgetting, alongside the con- tinuous improvements in generative models.
However, its application in class incremental object detection (CIOD) has been
significantly limited, primarily due to the com- plexities of scenes involving
multiple labels. In this paper, we propose a novel approach called stable
diffusion deep generative replay (SDDGR) for CIOD. Our method utilizes a
diffusion-based generative model with pre-trained text- to-diffusion networks
to generate realistic and diverse syn- thetic images. SDDGR incorporates an
iterative refinement strategy to produce high-quality images encompassing old
classes. Additionally, we adopt an L2 knowledge distilla- tion technique to
improve the retention of prior knowledge in synthetic images. Furthermore, our
approach includes pseudo-labeling for old objects within new task images, pre-
venting misclassification as background elements. Exten- sive experiments on
the COCO 2017 dataset demonstrate that SDDGR significantly outperforms existing
algorithms, achieving a new state-of-the-art in various CIOD scenarios. The
source code will be made available to the public.

本研究提出了一种名为稳定扩散深度生成重放（SDDGR）的新方法，它利用基于扩散的生成模型和预训练的文本到扩散网络生成逼真多样的合成图像，通过迭代优化策略生成高质量包含旧类别的图像，并采用 L2 知识蒸馏技术提高合成图像中先前知识的保存，并在新任务图像中利用伪标记技术对旧对象进行预测，防止其被误分类为背景元素。实验结果表明，SDDGR 在各种类别增量目标检测场景中显著优于现有算法，达到了最新的技术水平。源代码将向公众提供。