We then define the food-domain representativeness of different food databases in terms of the total number of images, number of classes of the domain and number of examples for class. Different features are then extracted from a CNN based on the Residual Network with 50 layers architecture and ...
Food image retrievalFaster R-CNN networkResearch on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years,......
In the life sciences, ontologies such as the Gene Ontology (GO) [1], Mondo [2], Uberon [3], and FoodOn [4] are used for a variety of purposes such as curation of gene function and expression, classification of diseases, or annotation of food datasets. Ontologies are core components ...
Advances in light-sheet and confocal microscopy now allow imaging of cleared large biological tissue samples and enable the 3D appreciation of cell and protein localization in their native organ environment. However, the sample preparations for such imag
Given robust cross-participant classification of memory states (Fig. 2A), the first goal was to measure trial-level retrieval state evidence across time in the attention task. To the extent that internal attention is a central process of the retrieval state, there should be temporal dissociation...
Image-Recipe retrieval is the task of retrieving closely related recipes from a collection given a food image and vice versa. The modality gap between images and recipes makes it a challenging task. Recent studies usually focus on learning consistent image and recipe representations to bridge the ...
restaurant has great food #yum #restaurant Command: $./starspace train -trainFile input.txt -model tagspace -label '#' Example scripts: We apply the model to the problem of text classification on AG's News Topic Classification Dataset. Here our tags are news article categories, and we ...
For ImageNet V2, we use the same training data of ImageNet for the linear probe classification experiments. For COCO and LVIS, we only use them to evaluate the zero-shot ROI classification, thus we don't need training data. The classification datasets use classification accuracy as e...
etc. is collected using Google Forms. The data contain information about daily sleeping patterns, daily heart rate, sport activities, logs of food consumed during the training period (from at least 2 participants) and self reported data like mode, stress, fatigue, readiness to train and other ...
Data Mining and Knowledge Discovery. Sept, 1998. Google Scholar Berkeley Digital Library Project http://elib.cs.berkeley.edU/src/cypress/meets.c Koutsougeras, C. and C.A. Papachristou, “Training of A Neural Network Model for Pattern Classification Based on an Entropy Measure”, ...