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Found 60 papers, 16 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
A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients' disorders and provide guidance during preoperative and intra-operative procedures.
Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder
In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps.
Continual Evidential Deep Learning for Out-of-Distribution Detection
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions.
Commentary on explainable artificial intelligence methods: SHAP and LIME
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning models into a more digestible form.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Characterizing the contribution of dependent features in XAI methods
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Pre-NeRF 360: Enriching Unbounded Appearances for Neural Radiance Fields
Neural radiance fields (NeRF) appeared recently as a powerful tool to generate realistic views of objects and confined areas.
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
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction
Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities.
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
STPGANsFusion: Structure and Texture Preserving Generative Adversarial Networks for Multi-modal Medical Image Fusion
Next, the fusion of the structure and the texture components is carried out using two generative adversarial networks (GANs) consisting of a generator and two discriminators to get fused structure and texture components.
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation
Given the hyper-spectral imaging unique potentials in grasping the polymer characteristics of different materials, it is commonly used in sorting procedures.
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation
In this paper, we propose Layer Ensembles, a novel uncertainty estimation method that uses a single network and requires only a single pass to estimate predictive uncertainty of a network.
Modeling long-term interactions to enhance action recognition
In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels.
Eating Habits Discovery in Egocentric Photo-streams
Eating habits are learned throughout the early stages of our lives.
Behavioural pattern discovery from collections of egocentric photo-streams
Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach.
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
Automatic Reminiscence Therapy for Dementia
With people living longer than ever, the number of cases with dementia such as Alzheimer's disease increases steadily.
Seeing and Hearing Egocentric Actions: How Much Can We Learn?
Our interaction with the world is an inherently multimodal experience.
SLSNet: Skin lesion segmentation using a lightweight generative adversarial network
Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model.
How Much Does Audio Matter to Recognize Egocentric Object Interactions?
Sounds are an important source of information on our daily interactions with objects.
Social Relation Recognition in Egocentric Photostreams
This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera 2fpm, by relying solely on what the camera is seeing.
Towards Egocentric Person Re-identification and Social Pattern Analysis
We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams.
Towards Unsupervised Familiar Scene Recognition in Egocentric Videos
Nowadays, there is an upsurge of interest in using lifelogging devices.
Unsupervised routine discovery in egocentric photo-streams
The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person's health.
Towards Emotion Retrieval in Egocentric PhotoStream
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras.
Hierarchical approach to classify food scenes in egocentric photo-streams
Recent studies have shown that the environment where people eat can affect their nutritional behaviour.
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams
Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos.
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams.
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network
Diversity of food and its attributes represents the culinary habits of peoples from different countries.
Cultural Vocal Bursts Intensity Prediction
General Classification
+1
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
Smartphone picture organization: A hierarchical approach
Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud.
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.
Batch-based Activity Recognition from Egocentric Photo-Streams Revisited
Wearable cameras can gather large a\-mounts of image data that provide rich visual information about the daily activities of the wearer.
Social Style Characterization from Egocentric Photo-streams
This paper proposes a system for automatic social pattern characterization using a wearable photo-camera.
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.
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.
Detecting Hands in Egocentric Videos: Towards Action Recognition
Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras.
Towards social pattern characterization in egocentric photo-streams
Following the increasingly popular trend of social interaction analysis in egocentric vision, this manuscript presents a comprehensive study for automatic social pattern characterization of a wearable photo-camera user, by relying on the visual analysis of egocentric photo-streams.
Batch-Based Activity Recognition from Egocentric Photo-Streams
However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames.
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
Recognizing Activities of Daily Living from Egocentric Images
Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services.
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.
Clothing and People - A Social Signal Processing Perspective
In our society and century, clothing is not anymore used only as a means for body protection.
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.
Sentiment Recognition in Egocentric Photostreams
Lifelogging is a process of collecting rich source of information about daily life of people.
All the people around me: face discovery in egocentric photo-streams
Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data.
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.
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations.
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams
Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera.
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.
Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos
In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera.
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.
No more meta-parameter tuning in unsupervised sparse feature learning
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity.
Ranked #104 on
Image Classification
on STL-10
