Learning image query concepts via intelligent sampling ; Article #: ; Date of Conference: 22-25 August 2001 ; Date Added to IEEE Xplore: 20 October 2003.
In this paper, we propose an active and inductive combined learning method to learn users' image query concepts. We model query concepts in K-CNF, ...
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An active and inductive combined learning method to learn users' image query concepts that maximizes the usefulness of each example it generates for ...
In this paper, we propose an active and inductive combined learning method to learn users' image query concepts. We model query concepts in -CNF, ...
Beitao Li, Edward Y. Chang, Chung-Sheng Li: Learning Image Query Concepts via Intelligent Sampling. ICME 2001. a service of Schloss Dagstuhl - Leibniz ...
Through the learner frame, PBIR learns what the user wants via an intelligent sampling process. ... Learning image query concepts via intelligent sampling.
This research project will advance intelligent search engines by developing query-concept learners. Techniques for detecting concept drifts as well as multi- ...
In this work, we show how language can be a proxy for conceptual similarity; allowing us to sample better pairs for contrastive learning and train more ...
In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content ...
Jan 3, 2024 · This research paper introduces innovative selection methods within the Active Learning framework, aiming to identify informative images from unlabeled datasets.