Search Results for author:
Found 9 papers, 2 papers with code
Learning Diverse Features in Vision Transformers for Improved Generalization
We observe improved out-of-distribution performance on diagnostic benchmarks (MNIST-CIFAR, Waterbirds) as a consequence of the enhanced diversity of features and the pruning of undesirable heads.
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve the performance of individual, per dataset, student models.
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
Detecting abnormal events in video using Narrowed Normality Clusters
To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality.
Ranked #15 on
Anomaly Detection
on CUHK Avenue
Unmasking the abnormal events in video
We propose a novel framework for abnormal event detection in video that requires no training sequences.
Ranked #29 on
Anomaly Detection
on CUHK Avenue
Abnormal Event Detection In Video
Authorship Verification
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How hard can it be? Estimating the difficulty of visual search in an image
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task.
Searching for objects driven by context
The dominant visual search paradigm for object class detection is sliding windows.
Exploiting spatial overlap to efficiently compute appearance distances between image windows
We present a computationally efficient technique to compute the distance of high-dimensional appearance descriptor vectors between image windows.
