Understanding deep learning techniques for image segmentation
The machine learning community has been overwhelmed by a plethora of deep learning--
based approaches. Many challenging computer vision tasks, such as detection, localization …
based approaches. Many challenging computer vision tasks, such as detection, localization …
A brief introduction to weakly supervised learning
ZH Zhou - National science review, 2018 - academic.oup.com
Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …
number of training examples, where each training example has a label indicating its ground …
Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning
Automated Screening of COVID-19 from chest CT is of emergency and importance during
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …
the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 …
Multiple instance learning: A survey of problem characteristics and applications
Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …
Sun rgb-d: A rgb-d scene understanding benchmark suite
S Song, SP Lichtenberg, J ** (DTW) distance is a strong solution in …
Automatic linguistic indexing of pictures by a statistical modeling approach
Automatic linguistic indexing of pictures is an important but highly challenging problem for
researchers in computer vision and content-based image retrieval. In this paper, we …
researchers in computer vision and content-based image retrieval. In this paper, we …
MILES: Multiple-instance learning via embedded instance selection
Multiple-instance problems arise from the situations where training class labels are attached
to sets of samples (named bags), instead of individual samples within each bag (called …
to sets of samples (named bags), instead of individual samples within each bag (called …
Multi-instance learning by treating instances as non-iid samples
Previous studies on multi-instance learning typically treated instances in the bags as
independently and identically distributed. The instances in a bag, however, are rarely …
independently and identically distributed. The instances in a bag, however, are rarely …