A multi-task learning approach for meal assessment

Lu, Ya; Allegra, Dario; Anthimopoulos, Marios; Stanco, Filippo; Farinella, Giovanni Maria; Mougiakakou, Stavroula

doi:10.1145/3230519.3230593

Cited by 38 publications

(22 citation statements)

References 39 publications

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“…The GoCARB system is an initial attempt to achieve practical estimation of the food volume in real scenarios and has been validated both technically [ 22 ] and in a framework of pre-clinical and clinical trials [ 15 , 23 ]. Following the development and great progress of the CNNs, a number of recent studies have tried to address the estimation of food volume using single view colour images [ 14 , 24 , 25 , 26 ]. Ref.…”

Section: Introductionmentioning

confidence: 99%

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goFOODTM: An Artificial Intelligence System for Dietary Assessment

Stathopoulou

Vasiloglou

et al. 2020

Sensors

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Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.

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Section: Introductionmentioning

confidence: 99%

“…Ref. [ 14 , 26 ] uses the CNNs predicting the depth image from single-view colour image, while the predicted depth map is used for the food volume calculation. Ref.…”

Section: Introductionmentioning

confidence: 99%

goFOODTM: An Artificial Intelligence System for Dietary Assessment

Stathopoulou

Vasiloglou

et al. 2020

Sensors

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“…al. [33] to address food segmentation, recognition, and volume estimation, which successfully outperforms the baseline methods. Another MTL architecture with heavy sharing of weights and features was introduced in [34] to perform four tasks: 2D pose estimation, 3D pose estimation, 2D action recognition, and 3D action recognition.…”

Section: Multi-task Learningmentioning

confidence: 99%

Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations

Sulistiyo

Kawanishi

Deguchi

et al. 2020

IEICE Trans. Fundamentals

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Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object's class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.

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“…In recent days, object detection is being used for so many applications. There are some state-of-the-arts which work for different types of object detection such as flower detection [8], fruit detection [9,10], food segmentation and detection [11] cats and dogs detection [12] etc. The main goal of all these detection algorithms is to obtain higher efficiency and cover different complex use cases by overcoming different limitations.…”

Section: Introductionmentioning

confidence: 99%

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

Khan¹,

Debnath²

2019

IJIGSP

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In this paper, an efficient approach has been proposed to localize every clearly visible object or region of object from an image, using less memory and computing power. For object detection we have processed every input image to overcome several complexities, which are the main limitations to achieve better result, such as overlap between multiple objects, noise in the image background, poor resolution etc. We have also implemented an improved Convolutional Neural Network based classification or recognition algorithm which has proved to provide better performance than baseline works. Combining these two detection and recognition approaches, we have developed a competent multi-class Fruit Detection and Recognition (FDR) model that is very proficient regardless of different limitations such as high and poor image quality, complex background or lightening condition, different fruits of same shape and color, multiple overlapped fruits, existence of non-fruit object in the image and the variety in size, shape, angel and feature of fruit. This proposed FDR model is also capable of detecting every single fruit separately from a set of overlapping fruits. Another major contribution of our FDR model is that it is not a dataset oriented model which works better on only a particular dataset as it has been proved to provide better performance while applying on both real world images (e.g., our own dataset) and several states of art datasets. Nevertheless, taking a number of challenges into consideration, our proposed model is capable of detecting and recognizing fruits from image with a better accuracy and average precision rate of about 0.9875.

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A multi-task learning approach for meal assessment

Cited by 38 publications

References 39 publications

goFOODTM: An Artificial Intelligence System for Dietary Assessment

goFOODTM: An Artificial Intelligence System for Dietary Assessment

Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations

Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques

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