2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-2.5
Paper Title DEEP ACTIVE LEARNING FROM MULTISPECTRAL DATA THROUGH CROSS-MODALITY PREDICTION INCONSISTENCY
Authors Heng Zhang, Elisa Fromont, Univ Rennes, France; Sebastien Lefevre, Univ Bretagne Sud, France; Bruno Avignon, ATERMES, France
SessionMLR-APPL-IVSMR-2: Machine learning for image and video sensing, modeling and representation 2
LocationArea D
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
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Abstract Data from multiple sensors provide independent and complementary information, which may improve the robustness and reliability of scene analysis applications. While there exist many large-scale labelled benchmarks acquired by a single sensor, collecting labelled multi-sensor data is more expensive and time-consuming. In this work, we explore the construction of an accurate multispectral (here, visible & thermal cameras) scene analysis system with minimal annotation efforts via an active learning strategy based on the cross-modality prediction inconsistency. Experiments on multispectral datasets and vision tasks demonstrate the effectiveness of our method. In particular, with only 10% of labelled data on KAIST multispectral pedestrian detection dataset, we obtain comparable performance as other fully supervised State-of-the-Art methods.