Tag Archives: paper

FAME: Face Association through Model Evolution

Here, I summarize a new method called FAME for learning Face Models from noisy set of web images. I am studying this for my MS Thesis. To be a little intro to my thesis, the title is "Mining Web Images for Concept Learning" and it introduces two new methods for automatic learning of visual concepts from noisy web images. First proposed method is FAME and the other work was presented here before, that is namely ConceptMap and it is accepted for ECCV14 (self promotion :)).

Before I start, I should disclaim that FAME is not a fully furnished work and waiting your valuable comments. Please leave your statements about anything you find useful, ridiculous, awkward or great.

In this work, we grasp the problem of learning face models for public faces from images collected from web through querying a particular person name. Collected images are called weakly-labelled by the rough prescription of defined query. However, the data is very noisy even after face detection, with false detections or several irrelevant faces Continue reading FAME: Face Association through Model Evolution


Our ECCV2014 work "ConceptMap: Mining noisy web data for concept learning"

---- I am living the joy of seeing my paper title on the list of accepted ECCV14 papers :). Seeing the outcome of your work makes worthwhile all your day to night efforts, REALLY!!!. Before start, I shall thank to my supervisor Pinar Duygulu for her great guidance.----

In this post, I would like to summarize the title work since I believe sometimes a friendly blog post might be more expressive than a solid scientific article.

"ConceptMap: Mining noisy web data for concept learning" proposes a pipeline so as to learn wide range of visual concepts by only defining a query to a image search engine. The idea is to query a concept at the service and download a huge bunch of images. Cluster images as removing the irrelevant instances. Learn a model from each of the clusters. At the end, each concept is represented by the ensemble of these classifiers. Continue reading Our ECCV2014 work "ConceptMap: Mining noisy web data for concept learning"