Official repo for our CVPR 22 paper: "Label, Verify, Correct: A Simple Few-Shot Object Detection Method" - prannaykaul/lvc
2020. Few-shot object detection with attention-RPN and multirelation detector. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20). 4012–4021. 2.3 标准化 文章提出目前最常用的用两种标准。 (1)VOC-07/12: 基于VOC07和VOC12数据集,在VOC07训练集...
Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from ...
1.2 Important Differences with Few-Shot Classification 1.3 FSOD Datasets 1.3.1 Pascal VOC 1.3.2 MS COCO 1.3.3 LVIS 1.3.4 Discussion 1.4 FSOD Evaluation Metrics 1.5 Few-Shot Object Detection Methods 1.5.1 Finetuning-only baselines 1.5.2 Conditioning-based methods 1.5.2.1 Prototype-based methods ...
The baseline is a simple detector based on the original SSD, with 320×320 input size and VGG-16 reduced backbone.由于M2Det由多个子组件组成,因此我们需要验证其对最终性能的有效性。 基线是基于原始SSD的简单检测器,具有320×320输入大小和VGG-16修剪的主干网络。
ZeroshotLearning,零次学习。 模型 对于训练集中没有出现过的类别,能自动创造出相应的映射:X→Y。 Low/Few-shot Learning、One-shot Learning,少/一次学习。 训练集中,每个类别都有样本,但都只是少量样本(甚至只有一个)。 摘要: 提出了一个Low-Shot Transfer Detector (LSTD),利用丰富的源领域(Source Domain)知...
Table 1: Detection accuracy comparisons in terms of mAP percentage on MS COCO test-dev set. Ablation study Since M2Det is composed of multiple subcomponents, we need to verify each of its effectiveness to the final performance. The baseline is a simple detector based on the original SSD, ...
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However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, ...