Publication

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Pascal Fua, Nikita Durasov, Weizhe Liu
2022
Conference Papers

Abstract

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which data annotation is either too costly or cannot be obtained fast enough.

Official source

https://infoscience.epfl.ch/entities/publication/20b369c5-48e5-4e1a-8eff-cc97b7b6a53c

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Pascal Fua, Nikita Durasov, Weizhe Liu
2022
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/20b369c5-48e5-4e1a-8eff-cc97b7b6a53c

About this result

Related concepts (20)

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

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