改进的one-shot目标检测的困难样本挖掘方法 原文:IMPROVED HARD EXAMPLE MINING APPROACH FOR SINGLE SHOT OBJECT DETECTORS 作者:Aybora Ko¨ksal★ O¨nder Tuzcuog˘lu★ Kutalmıs¸ Go¨kalp I˙nce★ Yoldas¸ Ataseven† A. Aydın Alatan★(cid:63) Center for Image Analysis (OGAM), Depar...
Group Co., Ltd {hanqing.yang, hlsheng, zhangyu80}@zju.edu.cn, jianqiang.jqh@gmail.com, ty640106@163.com {stephen.csj, dengbing.db, xiansheng.hxs}@alibaba-inc.com Abstract Instance-level feature matching is significantly impor- tant to the success of ...
Additionally, new characters appear every time a new volume of manga is released, making it impractical to re-train object detectors each time to detect these new characters. Therefore, one-shot object detection, where only a single query (reference) image is required to detect a new character...
1. MSDNN: Multi-Scale Deep Neural Network for Salient Object Detection(MSDNN: 基于多尺度深度神经网络的显著目标检测) 作者:Fen Xiao,Wenzheng Deng,Liangchan Peng,Chunhong Cao,Kai Hu,Xieping Gao 摘要:Salient object detection is a fundamental problem and has been received a great deal of attentions...
and then utilizes these noisy pseudo labels to train robust detectors. To handle the significant structure variations, we learn an end-to-end cascade of global alignment and local deformations, under the guidance of novel loss functions which incorporate edge information. In stage II, we explore ...
Choe J, Shim H (2019) Attention-based dropout layer for weakly supervised object localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2219–2228 Chong Y, Peng C, Zhang J, Pan S (2021) Style transfer for unsupervised domain-adaptive person re-ide...
can build corner detectors, or curved edge detectors, by detecting combinations of simpler edges.Through a buildup of feature complexity, eventually, higher layers can emerge which can detect complex, high-level features such as faces.The combinatorial size of the detectable feature space grows with...
and their relationships, and then generating textual descriptions, which heavily depends on pre-trained detectors and leads to performance drop when facing problems of heavy occlusion, tiny-size objects and long-tail in object detection. In... J Hou,X Wu,Y Qi,... 被引量: 0发表: 2019年 HC...
Object recognition from local scale-invariant features. In Proceedings of the Seventh IEEE International Conference on Computer Vision 1150–1157 (1999). Toshitaka, H., Hamido, F. & Andres, H. Less complexity one-class classification approach using construction error of convolutional image ...
A Bayesian approach to unsupervised one-shot learning of object categories. In Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, 13–16 October 2003; Volume 2, pp. 1134–1141. [Google Scholar] [CrossRef] Lake, B.M.; Salakhutdinov, R.; Tenenbaum, J.B...