Year: 2020
Type: conference-paper
Abstract: Multiple-instance learning (MIL) is a form of weakly supervised learning. Its instances are arranged in groups (called bags), and labels are provided for the entire bag after training. The classic MIL method represents examples with pre-calculated features, and the classification process is also cumbersome. In this paper, we use neural networks to extract features, parameterize all transformations, and use Bernoulli mixture model to construct MIL models for baggage tags, using simpler network structures and more accurately solving these problems. Experiments show that our results can be competitive with the classical MIL algorithm on the MINST dataset.
Authors: Lilin Yang, Wei Wu
Institution: Wuhan University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3095950629