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Found 26 papers, 10 papers with code
Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
To tackle the aforementioned challenges, in this paper, we design a novel uncertainty modeling framework for self-explaining networks, which not only demonstrates strong distribution-free uncertainty modeling performance for the generated explanations in the interpretation layer but also excels in producing efficient and effective prediction sets for the final predictions based on the informative high-level basis explanations.
Automated Natural Language Explanation of Deep Visual Neurons with Large Models
Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks.
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
The availability of handy multi-modal (i. e., RGB-D) sensors has brought about a surge of face anti-spoofing research.
Surveillance Face Anti-spoofing
In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks.
Face Presentation Attack Detection
Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment due to its convenience and high accuracy.
Flexible-Modal Face Anti-Spoofing: A Benchmark
Can we train a unified model, and flexibly deploy it under various modality scenarios?
Makeup216: Logo Recognition with Adversarial Attention Representations
One of the challenges of logo recognition lies in the diversity of forms, such as symbols, texts or a combination of both; further, logos tend to be extremely concise in design while similar in appearance, suggesting the difficulty of learning discriminative representations.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
Meta-Teacher For Face Anti-Spoofing
The meta-teacher is trained in a bi-level optimization manner to learn the ability to supervise the PA detectors learning rich spoofing cues.
3D High-Fidelity Mask Face Presentation Attack Detection Challenge
The threat of 3D masks to face recognition systems is increasingly serious and has been widely concerned by researchers.
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e. g., in cases of surveillance and photo-tagging).
Deep Learning for Face Anti-Spoofing: A Survey
Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs).
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask).
Searching for Alignment in Face Recognition
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the face image using a pre-defined template, extracting representations and comparing.
Layer-Wise Adaptive Updating for Few-Shot Image Classification
LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images.
Multi-Modal Face Anti-Spoofing Based on Central Difference Networks
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks.
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing.
Learning Meta Face Recognition in Unseen Domains
Face recognition systems are usually faced with unseen domains in real-world applications and show unsatisfactory performance due to their poor generalization.
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Here we propose a novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Ranked #4 on
Face Anti-Spoofing
on OULU-NPU
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Therefore, we define face anti-spoofing as a zero- and few-shot learning problem.
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning.
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.
Representation based and Attention augmented Meta learning
The method RAML aims to give the Meta learner the ability of leveraging the past learned knowledge to reduce the dimension of the original input data by expressing it into high representations, and help the Meta learner to perform well.
Exploiting temporal and depth information for multi-frame face anti-spoofing
Face anti-spoofing is significant to the security of face recognition systems.
A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning
Currently, many machine learning algorithms contain lots of iterations.
