Few-shot learning, i.e., learning novel concepts from few examples, is fundamental to practical visual recognition systems. While most of existing work has focused on few-shot classification, we make a step towards few-shot object detection, a more challenging yet under-explored task. We ...
Meta-Sim Learning to Generate Synthetic Datasets. ICCV 2019 Oral Meta-Learning to Detect Rare Objects. ICCV 2019 ECCV 2018 AAAI 2019 Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification. AAAI 2019 code - official (PyTorch) aims to deal with noisy data CVPR 2019...
59. Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning 会议:CVPR 2019. 作者:Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi 链接:https://openaccess.thecvf.com/content_CVPR_2019/papers/Wortsman_Learning_to_Learn_How_to_Learn_...
IDS is used to effectively detect all kinds of malicious attacks on the cyber and is one of the key systems to maintain cyber security. Although the machine learning model is widely used in intrusion detection and has made great progress in solving related problems, the traditional machine ...
head has been remodeled to detect or segment the objects that refer to the PRN’s inputs, including the category, po- sition, and structure information of low-shot objects. Our framework exactly boils down to a typical meta-learning paradigm, encouraging the name Meta R-CNN. Meta R-CNN ...
Wang, Y.X.; Ramanan, D.; Hebert, M. Meta-learning to detect rare objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 9925–9934. [Google Scholar] ...
Meta-learning to detect rare objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 9925–9934. [Google Scholar] Yan, J.; Wang, H.; Yan, M.; Diao, W.; Sun, ...
Finally, when data specific to the particular task are applied, the learner updates the model with a new set of weights. These latest updates are precisely targeted to detect our object of interest, reflecting the culmination of the learning process, where the model has acquired the necessary sp...
For instance, segmentation is used to detect infected tumor tissues in medical imaging modalities by separating tumor tissues from normal brain tissues and solid tumors [6]. Segmenting medical images is a demanding and time-consuming task [7]. Detecting abnormalities, particularly rare ones, is ...