Denis Baručić presents Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks

On 2020-10-23 11:00:00 at
Reading group on the work "Multi-Instance Classification by Max-Margin Training
of Cardinality-Based Markov Networks" (PAMI 2017) by Hossein Hajimirsadeghi and
Greg Mori presented by Dennis Baručić.

Paper abstract: We propose a probabilistic graphical framework for
multi-instance learning (MIL) based on Markov networks. This framework can deal
with different levels of labeling ambiguity (i.e., the portion of positive
instances in a bag) in weakly supervised data by parameterizing cardinality
potential functions. Consequently, it can be used to encode different
cardinality-based multi-instance assumptions, ranging from the standard MIL
assumption to more general assumptions. In addition, this framework can be
efficiently used for both binary and multiclass classification. To this end, an
efficient inference algorithm and a discriminative latent max-margin learning
algorithm are introduced to train and test the proposed multi-instance Markov
network models. We evaluate the performance of the proposed framework on binary
and multi-class MIL benchmark datasets as well as two challenging computer
vision tasks: cyclist helmet recognition and human group activity recognition.
Experimental results verify that encoding the degree of ambiguity in data can
improve classification performance

Paper URL:

Instructions for participants: The reading group studies the literature in the
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During the presentation we aim to have a fruitful discussion, a critical
analysis of the paper, as well as brainstorming for creative extensions.

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Za obsah zodpovídá: Petr Pošík