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Sergey K Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell

UC Berkeley; RWTH Aachen; MPI for Informatics; UC Berkeley

Poster: Timely Object Recognition

7:00pm – 12:00am Tuesday, December 04, 2012

Harrah’s Special Events Center 2nd Floor

This is part of the Poster Session which begins at 19:00 on Tuesday December 4, 2012

Tu80

In a large visual multi-class detection framework, the timeliness of results can be crucial. Our method for timely multi-class detection aims to give the best possible performance at any single point after a start time; it is terminated at a deadline time. Toward this goal, we formulate a dynamic, closed-loop policy that infers the contents of the image in order to decide which detector to deploy next. In contrast to previous work, our method significantly diverges from the predominant greedy strategies, and is able to learn to take actions with deferred values. We evaluate our method with a novel timeliness measure, computed as the area under an Average Precision vs. Time curve. Experiments are conducted on the eminent PASCAL VOC object detection dataset. If execution is stopped when only half the detectors have been run, our method obtains $66\%$ better AP than a random ordering, and $14\%$ better performance than an intelligent baseline. On the timeliness measure, our method obtains at least $11\%$ better performance. Our code, to be made available upon publication, is easily extensible as it treats detectors and classifiers as black boxes and learns from execution traces using reinforcement learning.

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