Molecules are complex dynamic objects that can exist in different molecular forms
(conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known …

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 …

Adaptive tracking-by-detection methods are widely used in computer vision for tracking
arbitrary objects. Current approaches treat the tracking problem as a classification task and …

Visual object tracking is a significant computer vision task which can be applied to many
domains, such as visual surveillance, human computer interaction, and video compression …

Text classification is a quintessential and practical problem in natural language processing
with applications in diverse domains such as sentiment analysis, fake news detection …

In this paper we propose a simple and effective way to integrate structural information in
random forests for semantic image labelling. By structural information we refer to the …

Most tracking-by-detection algorithms train discriminative classifiers to separate target
objects from their surrounding background. In this setting, noisy samples are likely to be …

In this paper, we tackle the problem of common object (multiple classes) discovery from a set
of input images, where we assume the presence of one object class in each image. This …

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as
a new tomographic technique to minimize the limitations of conventional digital …

While representation learning aims to derive interpretable features for describing visual
data, representation disentanglement further results in such features so that particular image …