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Found 14 papers, 7 papers with code
Generate to Adapt: Resolution Adaption Network for Surveillance Face Recognition
Although deep learning techniques have largely improved face recognition, unconstrained surveillance face recognition (FR) is still an unsolved challenge, due to the limited training data and the gap of domain distribution.
Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation
The blooming of social media and face recognition (FR) systems has increased people's concern about privacy and security.
AdvCloak: Customized Adversarial Cloak for Privacy Protection
This strategy initially focuses on adapting the masks to the unique individual faces via image-specific training and then enhances their feature-level generalization ability to diverse facial variations of individuals via person-specific training.
VIoTGPT: Learning to Schedule Vision Tools in LLMs towards Intelligent Video Internet of Things
Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data.
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples.
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
Ranked #2 on
Face Verification
on CALFW
OPOM: Customized Invisible Cloak towards Face Privacy Protection
In this paper, we investigate the face privacy protection from a technology standpoint based on a new type of customized cloak, which can be applied to all the images of a regular user, to prevent malicious face recognition systems from uncovering their identity.
Video Question Answering: Datasets, Algorithms and Challenges
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos.
MLFW: A Database for Face Recognition on Masked Faces
As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces.
Ranked #1 on
Face Recognition
on MLFW
Face Transformer for Recognition
Therefore, we investigate the performance of Transformer models in face recognition.
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
The training of a deep face recognition system usually faces the interference of label noise in the training data.
Towards Transferable Adversarial Attack against Deep Face Recognition
In particular, the existence of transferable adversarial examples can severely hinder the robustness of DCNNs since this type of attacks can be applied in a fully black-box manner without queries on the target system.
Adversarial Learning with Margin-based Triplet Embedding Regularization
The Deep neural networks (DNNs) have achieved great success on a variety of computer vision tasks, however, they are highly vulnerable to adversarial attacks.
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data
Large-scale face datasets usually exhibit a massive number of classes, a long-tailed distribution, and severe label noise, which undoubtedly aggravate the difficulty of training.
