Search Results for author:
Found 23 papers, 12 papers with code
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources.
Sea-Land-Cloud Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Satellites are increasingly adopting on-board AI for enhanced autonomy through in-orbit inference.
Learning Using Generated Privileged Information by Text-to-Image Diffusion Models
Learning Using Privileged Information is a particular type of knowledge distillation where the teacher model benefits from an additional data representation during training, called privileged information, improving the student model, which does not see the extra representation.
Masked Autoencoders for Unsupervised Anomaly Detection in Medical Images
Pathological anomalies exhibit diverse appearances in medical imaging, making it difficult to collect and annotate a representative amount of data required to train deep learning models in a supervised setting.
Audiovisual Masked Autoencoders
Can we leverage the audiovisual information already present in video to improve self-supervised representation learning?
Ranked #1 on
Audio Classification
on EPIC-KITCHENS-100
(using extra training data)
Diversity-Promoting Ensemble for Medical Image Segmentation
In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble.
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #2 on
Anomaly Detection
on CUHK Avenue
Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution
Our attention module uses the convolution operation to perform joint spatial-channel attention on multiple concatenated input tensors, where the kernel (receptive field) size controls the reduction rate of the spatial attention, and the number of convolutional filters controls the reduction rate of the channel attention, respectively.
Ranked #1 on
Image Super-Resolution
on IXI
Feature-level augmentation to improve robustness of deep neural networks to affine transformations
Recent studies revealed that convolutional neural networks do not generalize well to small image transformations, e. g. rotations by a few degrees or translations of a few pixels.
Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion
To this end, we study two knowledge distillation methods, one based on teacher-student training and one based on triplet loss.
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types.
Ranked #5 on
Anomaly Detection
on CUHK Avenue
(using extra training data)
CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation
Our neural model can be trained on unpaired images, due to the integration of a multi-level cycle-consistency loss.
A realistic approach to generate masked faces applied on two novel masked face recognition data sets
We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images.
Ranked #1 on
Face Recognition
on CASIA-WebFace+masks
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Ranked #2 on
Anomaly Detection
on UCSD Peds2
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+4
SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a Spatial Attention Module for Personal Protective Equipment Recognition
Typical approaches for PPE detection based on deep learning are (i) to train an object detector for items such as those listed above or (ii) to train a person detector and a classifier that takes the bounding boxes predicted by the detector and discriminates between people wearing and people not wearing the corresponding PPE items.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+2
Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion
First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Non-linear Neurons with Human-like Apical Dendrite Activations
In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer.
Ranked #7 on
Speech Emotion Recognition
on CREMA-D
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans
We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors.
Ranked #6 on
Image Super-Resolution
on IXI
Recognizing Facial Expressions of Occluded Faces using Convolutional Neural Networks
In this paper, we present an approach based on convolutional neural networks (CNNs) for facial expression recognition in a difficult setting with severe occlusions.
Facial Expression Recognition
Facial Expression Recognition (FER)
Clustering Images by Unmasking - A New Baseline
We propose a novel agglomerative clustering method based on unmasking, a technique that was previously used for authorship verification of text documents and for abnormal event detection in videos.
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training.
Ranked #14 on
Anomaly Detection
on ShanghaiTech
Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition.
Ranked #4 on
Facial Expression Recognition (FER)
on FER2013
(using extra training data)
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
