3 accepted papers
RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically datasets in mu…
Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its…
The success of multi-sensor data fusions in deep learning appears to be attributed to the use of complementary information among multiple sensor datasets. Compared to their predictive performance, relatively less attention has been devoted to the adversarial robustness of multi-sensor data fusion mo…