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Found 48 papers, 20 papers with code
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Moreover, MoE-FFD leverages the expressivity of transformers and local priors of CNNs to simultaneously extract global and local forgery clues.
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
The core component of Log-NCDEs is the Log-ODE method, a tool from the study of rough paths for approximating a CDE's solution.
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia.
Domain Generalization with Small Data
In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}.
Target-agnostic Source-free Domain Adaptation for Regression Tasks
Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data.
Robust Domain Misinformation Detection via Multi-modal Feature Alignment
We also propose a framework that simultaneously considers application scenarios of domain generalization (in which the target domain data is unavailable) and domain adaptation (in which unlabeled target domain data is available).
Pixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization.
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces.
NPVForensics: Jointing Non-critical Phonemes and Visemes for Deepfake Detection
Next, we design a phoneme-viseme awareness module for cross-modal feature fusion and representation alignment, so that the modality gap can be reduced and the intrinsic complementarity of the two modalities can be better explored.
The Age of Synthetic Realities: Challenges and Opportunities
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive.
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i. e., in-distribution (InD).
Ranked #2 on
Out-of-Distribution Detection
on ImageNet-1k vs Textures
(AUROC metric)
Interpretable Multimodal Misinformation Detection with Logic Reasoning
Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information.
Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less
To alleviate the forgetting of previous domains without using previous data, we propose the Proxy Prototype Contrastive Regularization (PPCR) to constrain the continual learning with previous domain knowledge from the proxy prototypes.
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation
Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc.
Temporal Coherent Test-Time Optimization for Robust Video Classification
To exploit information in video with self-supervised learning, TeCo uses global content from video clips and optimizes models for entropy minimization.
Robust Cross-domain CT Image Reconstruction via Bayesian Noise Uncertainty Alignment
Under this probabilistic framework, we propose to alleviate the noise distribution shifts between source and target domains via implicit noise modeling schemes in the latent space and image space, respectively.
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations
Empirical evaluation on a range of dynamic graph representation learning tasks demonstrates the superiority of our proposed approach compared to the baselines.
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage.
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications.
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning
Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences.
Toward domain generalized pruning by scoring out-of-distribution importance
Filter pruning has been widely used for compressing convolutional neural networks to reduce computation costs during the deployment stage.
Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement
In this paper, we propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks, where a post with low congruity can be identified as sarcasm.
Digital and Physical Face Attacks: Reviewing and One Step Further
With the rapid progress over the past five years, face authentication has become the most pervasive biometric recognition method.
Domain-invariant Feature Exploration for Domain Generalization
Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data.
One-Class Knowledge Distillation for Face Presentation Attack Detection
Under this framework, a teacher network is trained with source domain samples to provide discriminative feature representations for face PAD.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
Asymmetric Modality Translation For Face Presentation Attack Detection
In this paper, we propose a novel framework based on asymmetric modality translation for face presentation attack detection in bi-modality scenarios.
Learning Meta Pattern for Face Anti-Spoofing
To improve the generalization ability, recent hybrid methods have been explored to extract task-aware handcrafted features (e. g., Local Binary Pattern) as discriminative information for the input of DNNs.
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
The state-of-the-art deep neural networks are vulnerable to common corruptions (e. g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing).
Disentangled Feature Representation for Few-shot Image Classification
In this work, we propose a novel Disentangled Feature Representation framework, dubbed DFR, for few-shot learning applications.
Low-Light Image Enhancement with Normalizing Flow
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many.
Ranked #3 on
Low-Light Image Enhancement
on Sony-Total-Dark
Variational Disentanglement for Domain Generalization
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain.
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society.
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challenges that hinder their practical application.
Cross-Lingual Transfer Learning for Statistical Type Inference
Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data.
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered.
Neuron Coverage-Guided Domain Generalization
More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks.
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i. e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN).
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model.
Heterogeneous Domain Generalization via Domain Mixup
One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability.
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs).
Face Image Reflection Removal
Face images captured through the glass are usually contaminated by reflections.
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.
Domain Generalization With Adversarial Feature Learning
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an âunseenâ target domain by taking the advantage of multiple seen source-domain data.
Ranked #49 on
Domain Generalization
on PACS
