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pro vyhledávání: '"Ho, Kalun"'
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been
Externí odkaz:
http://arxiv.org/abs/2106.12303
Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These su
Externí odkaz:
http://arxiv.org/abs/2012.08803
Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information
Externí odkaz:
http://arxiv.org/abs/2008.07872
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discri
Externí odkaz:
http://arxiv.org/abs/2007.03123
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this wor
Externí odkaz:
http://arxiv.org/abs/2002.01192
Autor:
Ho, Kalun
Image clustering is one of the most important task of unsupervised learning in the area of computer vision. Deep learning approaches allow models to be trained on large datasets. In this thesis, image clustering objectives in the context of Triplet L
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15a47500ccc7f37a63a599ab5e89f841