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| November 29
| November 29
| Fatemeh Mir Rashed
| Fatemeh Mirrashed
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| Knowledge Adaptation in Visual Domains
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| December 6
| December 6
In this work we introduce an instance-level approach to multiple instance learning. Our bottom-up approach learns a discriminative notion of similarity between instances in positive bags and use it to form a discriminative similarity graph. We then introduce the notion of similarity preserving quasi-cliques that aims at discovering large quasi-cliques with high scores of within-clique similarities. We argue that such large cliques provide clue to infer the underlying structure between positive instances. We use a ranking function that takes into account pairwise similarities coupled with prospectiveness of edges to score all positive instances. We show that these scores yield to positive instance discovery. Our experimental evaluations show that our method outperforms state-of-the-art MIL methods both at the bag-level and instance-level predictions in standard benchmarks and image and text datasets.
In this work we introduce an instance-level approach to multiple instance learning. Our bottom-up approach learns a discriminative notion of similarity between instances in positive bags and use it to form a discriminative similarity graph. We then introduce the notion of similarity preserving quasi-cliques that aims at discovering large quasi-cliques with high scores of within-clique similarities. We argue that such large cliques provide clue to infer the underlying structure between positive instances. We use a ranking function that takes into account pairwise similarities coupled with prospectiveness of edges to score all positive instances. We show that these scores yield to positive instance discovery. Our experimental evaluations show that our method outperforms state-of-the-art MIL methods both at the bag-level and instance-level predictions in standard benchmarks and image and text datasets.
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===Knowledge Adaptation in Visual Domains===
Speaker: Fatemeh Mir Rashed -- Date: November 29, 2012
Speaker: Fatemeh Mirrashed -- Date: November 29, 2012
The new machine learning techniques of transfer learning and domain adaptation have recently captured special attention in the computer vision community. In this talk we will take a look at some of the methods that have been recently adopted or developed for adaptation of learning in the visual domains. We will also try to have an open discussion over some of more ideological questions such as better generalization versus adaptation. With abundance of massive volumes of visual training data should we keep at designing algorithms that could model all the possible variations in the visual world or should we regard adaptation as an integral part of learning in the visual domains?
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