Search Results for author:
Found 68 papers, 31 papers with code
FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba
In this paper, we propose FusionMamba, a novel dynamic feature enhancement method for multimodal image fusion with Mamba.
Multi-modal Document Presentation Attack Detection With Forensics Trace Disentanglement
Document Presentation Attack Detection (DPAD) is an important measure in protecting the authenticity of a document image.
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
This is particularly true for medical image segmentation (MIS) datasets, where the processes of collection and fine-grained annotation are time-intensive and laborious.
Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+3
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
This paper focuses on the challenge of answering questions in scenarios that are composed of rich and complex dynamic audio-visual components.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+5
AUFormer: Vision Transformers are Parameter-Efficient Facial Action Unit Detectors
Facial Action Units (AU) is a vital concept in the realm of affective computing, and AU detection has always been a hot research topic.
Ranked #1 on
Facial Action Unit Detection
on DISFA
A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection
Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades.
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks.
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
For the face forgery detection task, we evaluate GAN-based and diffusion-based data with both visual and acoustic modalities.
GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators.
Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement
Compared to image duplication and synthesis, image splicing detection is more challenging due to the lack of reference images and the typically small tampered areas.
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.
Hyperbolic Face Anti-Spoofing
To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks.
Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection
Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection.
Ranked #2 on
Facial Action Unit Detection
on DISFA
Visual Prompt Flexible-Modal Face Anti-Spoofing
Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems.
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localizing manipulated areas.
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT
There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects.
rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement
However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy.
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.
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.
Neuron Structure Modeling for Generalizable Remote Physiological Measurement
We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes.
Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning
Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively.
Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT
Our objectives were to (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; (3) demonstrate the utility of virtual clinical trials (VCTs) to evaluate DL-based methods.
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT
However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT.
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current FSCL methods are all for the class-incremental purpose.
Flexible-modal Deception Detection with Audio-Visual Adapter
Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud.
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing
In this paper, we investigate three key factors (i. e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth.
PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer
As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference.
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.
Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos
Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements.
Boosting Binary Neural Networks via Dynamic Thresholds Learning
For ViTs, DyBinaryCCT presents the superiority of the convolutional embedding layer in fully binarized ViTs and achieves 56. 1% on the ImageNet dataset, which is nearly 9% higher than the baseline.
Forensicability Assessment of Questioned Images in Recapturing Detection
We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues
In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues.
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously.
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization
To achieve this goal, we conducted a task-based characterization of a DL-based denoising approach for individual signal properties.
ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection
Instead of using coarse image patches with single-scale as in ViT, we propose the Multi-scale Multi-Head Self-Attention (MsMHSA) architecture to accommodate multi-scale patch partitions of Q, K, V feature maps to the heads of transformer in a coarse-to-fine manner, which enables to learn a fine-grained representation to perform pixel-level discrimination for face PAD.
Flexible-Modal Face Anti-Spoofing: A Benchmark
Can we train a unified model, and flexibly deploy it under various modality scenarios?
Review of Face Presentation Attack Detection Competitions
Face presentation attack detection (PAD) has received increasing attention ever since the vulnerabilities to spoofing have been widely recognized.
Geometry-Contrastive Transformer for Generalized 3D Pose Transfer
Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer
Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e. g., remote healthcare and affective computing).
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.
Pixel Difference Networks for Efficient Edge Detection
A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
Ranked #2 on
Edge Detection
on BRIND
iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
We introduce a new dataset for the emotional artificial intelligence research: identity-free video dataset for Micro-Gesture Understanding and Emotion analysis (iMiGUE).
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).
Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction
The framework is able to capture both local and long-range dependencies via the proposed attention mechanism for the learned appearance representations, which are further enriched by temporally attended physiological cues (remote photoplethysmography, rPPG) that are recovered from videos in the auxiliary task.
Dual-Cross Central Difference Network for Face Anti-Spoofing
In this paper, we propose two Cross Central Difference Convolutions (C-CDC), which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively.
TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection
3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks.
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).
A physics and learning-based transmission-less attenuation compensation method for SPECT
The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physics-based approach.
Medical Physics
Revisiting Pixel-Wise Supervision for Face Anti-Spoofing
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the presentation attacks (PAs).
NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems.
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
Gesture recognition has attracted considerable attention owing to its great potential in applications.
2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework
To address the problem of training on small datasets for action recognition tasks, most prior works are either based on a large number of training samples or require pre-trained models transferred from other large datasets to tackle overfitting problems.
Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling
Remote physiological measurements, e. g., remote photoplethysmography (rPPG) based heart rate (HR), heart rate variability (HRV) and respiration frequency (RF) measuring, are playing more and more important roles under the application scenarios where contact measurement is inconvenient or impossible.
Face Anti-Spoofing with Human Material Perception
In this paper we rephrase face anti-spoofing as a material recognition problem and combine it with classical human material perception [1], intending to extract discriminative and robust features for FAS.
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e. g., remote healthcare).
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.
The 1st Challenge on Remote Physiological Signal Sensing (RePSS)
Remote measurement of physiological signals from videos is an emerging topic.
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.
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
Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement
The method includes two parts: 1) a Spatio-Temporal Video Enhancement Network (STVEN) for video enhancement, and 2) an rPPG network (rPPGNet) for rPPG signal recovery.
Heart rate estimation
Photoplethysmography (PPG) heart rate estimation
+2
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks
Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG).
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.
Pedestrian re-identification based on Tree branch network with local and global learning
Deep part-based methods in recent literature have revealed the great potential of learning local part-level representation for pedestrian image in the task of person re-identification.
