Traditional sequential patterns do not take into account contextual information associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be “frequently, customers buy products A and B at the same time, and then buy product C”. Such a pattern does not consider the age, the gender or the socio-professional category of customers. How...

We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant...

Multi-Instance Learning (MIL) deals with problems where each training example is a bag, and each bag contains a set of instances. Multi-instance representation is useful in many real world applications, because it is able to capture more structural information than traditional flat single-instance representation. However, it also brings new challenges. Specifically, the distance between data ob...

Much of the potential industrial additive manufacturing (AM) is said to lie in digital specification components that can be transmitted seamlessly and unambiguously partners fostering flexible outsourcing. In industry, we observe nuanced AM supply chain governance structures result from make-or-buy decisions, with a tendency implement in-house. Thus, there discrepancy between what discussed lit...

We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. EM-DD is a general-purpose MI algorithm that can be applied with boolean or real-value labels and makes real-value predictions. On the boolean Musk benchmarks, the EM-DD algorithm without any tuning significantly outperforms all previous algorithms. EM-DD is relatively ins...