Large scale visual food recognition
Food recognition plays an important role in food choice and intake, which is essential to the
health and well‐being of humans. It is thus of importance to the computer vision community …
health and well‐being of humans. It is thus of importance to the computer vision community …
[HTML][HTML] AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry
K Blöcher, R Alt - Electronic Markets, 2021 - Springer
The restaurant technology market is rapidly evolving and is transforming the restaurant
business as a significant sector of tourism and hospitality. Enabled by artificial intelligence …
business as a significant sector of tourism and hospitality. Enabled by artificial intelligence …
Rethinking few-shot object detection on a multi-domain benchmark
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-
training and few-shot learning datasets are from a similar domain. However, few-shot …
training and few-shot learning datasets are from a similar domain. However, few-shot …
Synthesizing knowledge-enhanced features for real-world zero-shot food detection
Food computing brings various perspectives to computer vision like vision-based food
analysis for nutrition and health. As a fundamental task in food computing, food detection …
analysis for nutrition and health. As a fundamental task in food computing, food detection …
Beyond Few-shot Object Detection: A Detailed Survey
V Chudasama, H Sarkar, P Wasnik… - arXiv preprint arXiv …, 2024 - arxiv.org
Object detection is a critical field in computer vision focusing on accurately identifying and
locating specific objects in images or videos. Traditional methods for object detection rely on …
locating specific objects in images or videos. Traditional methods for object detection rely on …
HyperSTAR: Task-Aware Hyperparameter Recommendation for Training and Compression
Hyperparameter optimization (HPO) methods alleviate the significant effort required to
obtain hyperparameters that perform optimally on visual learning problems. Existing …
obtain hyperparameters that perform optimally on visual learning problems. Existing …
Precision at Scale: Domain-Specific Datasets On-Demand
In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards
the utility of massive, general domain datasets for pretraining robust backbones. In this …
the utility of massive, general domain datasets for pretraining robust backbones. In this …
Food Detection and Recognition with Deep Learning: A Comparative Study
Food detection and recognition involves the use of computer vision and machine learning
techniques to identify and classify food items in images or videos. It has numerous …
techniques to identify and classify food items in images or videos. It has numerous …
Unleashing the Power of Transfer Learning for Visual Applications
C Liu - 2023 - search.proquest.com
Transfer learning is a prominent paradigm in machine learning, addressing the challenges
of model generalization across different domains. This dissertation delves into two …
of model generalization across different domains. This dissertation delves into two …