Search Results for author:
Found 9 papers, 4 papers with code
IEEE BigData 2023 Keystroke Verification Challenge (KVC)
Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3. 33% and 3. 61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD.
Undercover Deepfakes: Detecting Fake Segments in Videos
This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
Is Face Recognition Safe from Realizable Attacks?
Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well.
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data
Next, the latent representations for the succeeding instants obtained through non-linear transformations of these context vectors, are contrasted with the latent representations of the encoder for the multi-variables such that the density for the positive pair is maximized.
Continual Learning with Delayed Feedback
This work proposes a model-agnostic continual learning framework which can be used with neural networks as well as decision trees to incorporate continual learning.
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.
Ranked #1 on
Multi-Human Parsing
on PASCAL-Part
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.
Multiple-Human Parsing in the Wild
To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
Ranked #3 on
Multi-Human Parsing
on MHP v1.0
Correlation Filters with Limited Boundaries
In this paper, we propose a novel approach to correlation filter estimation that: (i) takes advantage of inherent computational redundancies in the frequency domain, and (ii) dramatically reduces boundary effects.
