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Found 47 papers, 20 papers with code
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT).
In-Bed Pose Estimation: A Review
Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare.
Audio-driven Talking Face Generation by Overcoming Unintended Information Flow
Specifically, this involves unintended flow of lip, pose and other information from the reference to the generated image, as well as instabilities during model training.
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
In this paper, we aim to address the large domain gap between high-resolution face images, e. g., from professional portrait photography, and low-quality surveillance images, e. g., from security cameras.
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
Bias-Aware Face Mask Detection Dataset
In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world.
OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusions
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022).
State of the Art of Audio- and Video-Based Solutions for AAL
The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation.
Face-Dubbing++: Lip-Synchronous, Voice Preserving Translation of Videos
The system is designed to combine multiple component models and produces a video of the original speaker speaking in the target language that is lip-synchronous with the target speech, yet maintains emphases in speech, voice characteristics, face video of the original speaker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
VIDI: A Video Dataset of Incidents
To assess the performance of the recent state-of-the-art approaches, Vision Transformer and TimeSformer, as well as to explore the contribution of video-based information for incident classification, we performed benchmark experiments on the VIDI and Incidents Dataset.
Exposure Correction Model to Enhance Image Quality
We show that after applying exposure correction with the proposed model, the portrait matting quality increases significantly.
A Mobile Food Recognition System for Dietary Assessment
With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.
Fight Detection from Still Images in the Wild
We also propose a new dataset, named Social Media Fight Images (SMFI), comprising real-world images of fight actions.
Shuffled Patch-Wise Supervision for Presentation Attack Detection
We tested the proposed method both on the standard benchmark datasets -- Replay-Mobile, OULU-NPU -- and on a real-world dataset.
On Recognizing Occluded Faces in the Wild
In this paper, we present the Real World Occluded Faces (ROF) dataset, that contains faces with both upper face occlusion, due to sunglasses, and lower face occlusion, due to masks.
Alpha Matte Generation from Single Input for Portrait Matting
We first generate a coarse segmentation map from the input image and then predict the alpha matte by utilizing the image and segmentation map.
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19
To train and evaluate the developed system, we collected and annotated images that represent face mask usage and face-hand interaction in the real world.
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection
Indeed, differently from commonly used approaches that consider a neural network as a single computational block, i. e., using the output of the last layer only, MOCCA explicitly leverages the multi-layer structure of deep architectures.
Ranked #80 on
Anomaly Detection
on MVTec AD
Words as Art Materials: Generating Paintings with Sequential GANs
These variations in images provide originality which is an important factor for artistic essence.
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
First, we train a network to transform real LR images to the space of bicubically downsampled images in a supervised manner, by using both real LR/HR pairs and synthetic pairs.
Ear2Face: Deep Biometric Modality Mapping
We have achieved very promising results, especially on the FERET dataset, generating visually appealing face images from ear image inputs.
Offline Signature Verification on Real-World Documents
Research on offline signature verification has explored a large variety of methods on multiple signature datasets, which are collected under controlled conditions.
Thermal to Visible Face Recognition Using Deep Autoencoders
Also, we assess the impact of alignment in thermal to visible face recognition.
Ranked #1 on
Face Recognition
on Carl
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
By benefiting from perceptual losses, recent studies have improved significantly the performance of the super-resolution task, where a high-resolution image is resolved from its low-resolution counterpart.
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem.
Multimodal Age and Gender Classification Using Ear and Profile Face Images
Experimental results indicated that profile face images contain a rich source of information for age and gender classification.
Exploring Factors for Improving Low Resolution Face Recognition
By leveraging this information, we have utilized deep face models trained on MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art accuracies on the SCFace and ICB-RW benchmarks, even without using any training data from the datasets of these benchmarks.
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.
Learn to synthesize and synthesize to learn
To overcome these shortcomings, we propose attribute guided face image generation method using a single model, which is capable to synthesize multiple photo-realistic face images conditioned on the attributes of interest.
The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC).
Age and Gender Classification From Ear Images
Although there have been a few previous work on gender classification using ear images, to the best of our knowledge, this study is the first work on age classification from ear images.
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training.
Ranked #4 on
Image Dehazing
on O-Haze
Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks
We have first shown the importance of domain adaptation, when deep convolutional neural network models are used for ear recognition.
A Computer Vision System to Localize and Classify Wastes on the Streets
Littering quantification is an important step for improving cleanliness of cities.
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available.
Combining Multiple Views for Visual Speech Recognition
In this paper, we explore this aspect and provide a comprehensive study on combining multiple views for visual speech recognition.
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches.
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce in the literature.
Exploiting Convolution Filter Patterns for Transfer Learning
In this paper, we introduce a new regularization technique for transfer learning.
Face Deidentification with Generative Deep Neural Networks
Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.
How Transferable are CNN-based Features for Age and Gender Classification?
Domain specific VGG-Face CNN model has been found to be more useful and provided better performance for both age and gender classification tasks, when compared with generic AlexNet-like model, which shows that transfering from a closer domain is more useful.
How Image Degradations Affect Deep CNN-based Face Recognition?
This is particularly important, since in real-world face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion.
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition
Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets.
Apparent Age Estimation Using Ensemble of Deep Learning Models
To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range.
