Multi-instance multi-label learning (MIML) refers to the
learning problems where each example is represented by a
bag/collection of instances and is labeled by multiple labels.
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Rong Jin (Michigan State University), Shijun Wang...
Central to many problems in scene understanding based on using a network of tens, hundreds or even thousands of randomly distributed cameras with on-board processing and wireless c...
Shubao Liu, Kongbin Kang, Jean-Philippe Tarel and ...
Robust real-time tracking of non-rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this t...
An ad hoc data source is any semistructured data source for which useful data analysis and transformation tools are not readily available. Such data must be queried, transformed a...
Kathleen Fisher, David Walker, Kenny Qili Zhu, Pet...