Although matching features between different media is challenging, we believe cross-media is the tendency for AV relocalization since its low cost and accuracy can be comparable to the same-sensor-based methods.
Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies.
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs.
To encourage further progress in food recognition, we introduce the dataset ISIA Food- 500 with 500 categories from the list in the Wikipedia and 399, 726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume.