incremental few-shot learning论文阅读 incremental few-shot learning 论文的主要目的:在不使用原始训练数据的情况下,对新增加类别的少量数据进行训练以进行增量学习。 Related Work: object detection 作者比较了一阶段和二阶段检测模型,并讲述了一般检测模型的不足。检测模型需要大量有标注的数据进行训练,当线上部署的...
Abstract 作者提出了一个新的领域:Open World Object Detection,该任务定义为:1.测试集中可能出现未知类别的物体,网络需将其识别为unknown类别 2. 如果之后给出某个未知的标签,需要网络能够增量学习新的类别 目前已有类似领域为: Open Set Classification:该领域所有工作虽然可以识别unknown类别,但是它们不能在多个训练e...
We present T-Rex2, a highly practical model for open-set object detection. Previous open-set object detection methods relying on text prompts effectively encapsulate the abstract concept of common objects, but struggle with rare or complex object represe
5831 3. Open World Object Detection Let us formalise the definition of Open World Object De- tection in this section. At any time t, we consider the set of known object classes as Kt = {1, 2, .., C} ⊂ N+ where N+ denotes the set of positive integers. In order to realis- ...
A simple framework for open-vocabulary segmentation and detection. In ICCV, pages 1020–1031, 2023. 3, 6 [81] Lingzhi Zhang, Shenghao Zhou, Simon Stent, and Jianbo Shi. Fine-grained egocentric hand-object segmentation: Dataset, model, and applications. In European Conference on Computer Vision...
In federated settings, clients often grapple with limited or imbalanced datasets, especially in federated few-shot learning contexts [79]. Such data scarcity can result in suboptimal model performance, as it may not fully capture the diversity of the data distribution [80]. Moreover, privacy conce...
The detection process of object 3 is the same 7) Generate anchor size by K-means clustering method The anchor size set in the original YOLOv4-Tiny is a generic one, which is designed by taking into account various considerations, and therefore may not be the most suitable anchor size for ...
MMLU focuses on zero-shot and few-shot evaluation, making it more similar to how we evaluate humans. It covers 57 areas across STEM, the humanities, the social sciences, and more, testing for both knowledge and problem-solving skills.
Secondly, if an object detection model has been trained on certain classes and you want to add an extra class, you would need to label this new class in your data and retrain the model. CLIP’s ability to combine natural language and image features in combination with its zero-shot ...
To show the cross-domain knowledge transfer ability and the out-of-domain imagination ability of our pre-trained BriVL, we conduct zero-shot experiments on two remote sensing scene classification benchmarks. The first dataset is UC Merced Land-Use (UCM)36, which has 21 classes and 100 images...