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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...

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Related Experiment Video

Updated: May 17, 2026

Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Coaching the exploration and exploitation in active learning for interactive video retrieval.

Xiao-Yong Wei¹, Zhen-Qun Yang

¹College of Computer Science, Sichuan University, Chengdu 610065, China. cswei@scu.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 13, 2012

Summary

Coached active learning makes query distribution predictable for interactive video retrieval. This approach improves exploration and exploitation, outperforming conventional methods in large-scale tests.

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Area of Science:

Computer Science
Information Retrieval
Machine Learning

Background:

Active learning in interactive video/image retrieval struggles with unknown query distributions.
This uncertainty creates a dilemma between exploring feature space and exploiting known relevant instances.

Purpose of the Study:

To introduce a novel coached active learning approach for predictable query distribution in interactive retrieval.
To enable principled scheduling of exploration and exploitation timing and step sizes.

Main Methods:

Developed a coached active learning framework that estimates and predicts query distribution through training.
Utilized the estimated distribution for strategic guidance of search and relevance gathering.

Main Results:

Validated the approach on a large-scale dataset (TRECVID 2005-2009).
Demonstrated robust performance against domain shift.
Outperformed eight conventional active learning methods and six state-of-the-art interactive video retrieval systems.

Conclusions:

Coached active learning effectively addresses the challenge of unknown query distributions in interactive retrieval.
The method offers a principled and efficient strategy for balancing exploration and exploitation.
Achieved superior performance compared to existing active learning and retrieval systems.