Natural Language Person Search Using Deep Reinforcement Learning
Published in arXiv preprint arXiv:1809.00365, 2018
We present a method for person search in videos using natural language descriptions and deep reinforcement learning. Our approach formulates person search as a sequential decision-making problem where an agent learns to efficiently navigate through video frames to locate individuals matching natural language queries. The reinforcement learning framework enables the model to develop effective search strategies that balance exploration and exploitation, resulting in faster and more accurate person retrieval compared to exhaustive search baselines.
Recommended citation: @article{shah2018natural, title={Natural Language Person Search Using Deep Reinforcement Learning}, author={Shah, Ankit and Vuong, Tyler}, journal={arXiv preprint arXiv:1809.00365}, year={2018} }
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