Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

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Collaborative Learning with Pseudo Labels for Robust Classification in the Presence of Noisy Labelslabels) can deteriorate supervised learning performance significantly as it makes models to be trained with wrong targets. There are technics to train models in the presence of noise in data labels, but they usually suffer from the data inefficiency or overhead of additional steps. In this work, we

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Challenges from Fast Camera Motion and Image Blur: Dataset and EvaluationIn our dataset, image sequences with different camera speeds featuring the same scene and the same camera trajectory. To synthesize a photo-realistic image sequence with fast camera motions, we propose an image blur synthesis method that generates blurry images by their sharp images, camera motions

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Self-supervised Attribute-Aware Refinement Network for Low-Quality Text Recognitionegular shapes. Training a model for text recognition with such types of degradations is notoriously hard. In this work, we analyze this problem in terms of two attributes: semantic and a geometric attribute, which are crucial cues for describing low-quality text. To handle this issue, we propose a n

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0302-9743 he 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic..The 249 full papers, 18 short papers, and 21 further contributions included in the workshop pro

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Nicole Schneeweis,Rudolf Winter-Ebmern algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects.

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Reinforcement Learning for Improving Object Detectionn algorithm called ObjectRL to choose the amount of a particular pre-processing to be applied to improve the object detection performances of pre-trained networks. The main motivation for ObjectRL is that an image which looks good to a human eye may not necessarily be the optimal one for a pre-trained object detector to detect objects.