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Found 19 papers, 9 papers with code
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition
Trained through an end-to-end multi-task learning process, this method enhances performance in the fine-grained food recognition task, showing exceptional prowess with highly similar classes.
Ranked #4 on
Fine-Grained Image Classification
on Food-101
Fine-Grained Image Classification
Fine-Grained Image Recognition
+2
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Extensive experimentation shows improvements in the SoTA FGVR benchmarks of up to +1. 3% of accuracy using both CNNs and transformer-based networks.
Fine-Grained Image Recognition
Fine-Grained Visual Recognition
Learning Multi-Subset of Classes for Fine-Grained Food Recognition
We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets.
Ranked #1 on
Fine-Grained Image Classification
on FoodX-251
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis.
Exploring Food Detection using CNNs
One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption.
Food Recognition using Fusion of Classifiers based on CNNs
With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years.
Serious Games Application for Memory Training Using Egocentric Images
Mild cognitive impairment is the early stage of several neurodegenerative diseases, such as Alzheimer's.
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
R-Clustering for Egocentric Video Segmentation
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework.
Egocentric Video Description based on Temporally-Linked Sequences
We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences.
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering
In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet.
Can a CNN Recognize Catalan Diet?
In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68. 07\%, and top-5 of 89. 53\%, for a total of 115+101 food classes.
Simultaneous Food Localization and Recognition
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns.
Video Description using Bidirectional Recurrent Neural Networks
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes.
Semantic Summarization of Egocentric Photo Stream Events
With the rapid increase of users of wearable cameras in recent years and of the amount of data they produce, there is a strong need for automatic retrieval and summarization techniques.
Towards Storytelling from Visual Lifelogging: An Overview
Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time.
Visual Summary of Egocentric Photostreams by Representative Keyframes
Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e. g. memory reinforcement).
Ego-Object Discovery
Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images.
