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Found 31 papers, 20 papers with code
Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking
Malocclusion is a major challenge in orthodontics, and its complex presentation and diverse clinical manifestations make accurate localization and diagnosis particularly important.
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
We also design a \textbf{U}-shaped \textbf{D}ual \textbf{S}pace \textbf{A}daptation (\textbf{U-DSA}) module to enhance the adaptability of representations while maintaining generalization performance.
PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing
Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling.
SPF-Portrait: Towards Pure Portrait Customization with Semantic Pollution-Free Fine-tuning
Fine-tuning a pre-trained Text-to-Image (T2I) model on a tailored portrait dataset is the mainstream method for text-driven customization of portrait attributes.
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance.
DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs.
BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks.
CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect beyond the editing regions.
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain.
SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection.
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA).
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model
This significantly enhances the report generation capability and clinical effectiveness of multi-modal large language models in the field of radiology reportgeneration.
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
However, current FER paradigms face challenges in generalization, lack semantic information aligned with natural language, and struggle to process both images and videos within a unified framework, making their application in multimodal emotion understanding and human-computer interaction difficult.
Facial Expression Recognition
Facial Expression Recognition (FER)
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions.
Ranked #2 on
Facial Action Unit Detection
on BP4D
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.
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems.
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Sound events in daily life carry rich information about the objective world.
MRecGen: Multimodal Appropriate Reaction Generator
Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour.
ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions
To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours.
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW).
Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition
Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i. e., they make predictions mainly based on texture cues.
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data.
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator
\noindent Traditional L_p norm-restricted image attack algorithms suffer from poor transferability to black box scenarios and poor robustness to defense algorithms.
Scene Consistency Representation Learning for Video Scene Segmentation
Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods.
Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display.
Ranked #4 on
Facial Action Unit Detection
on BP4D
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations.
Group-wise Inhibition based Feature Regularization for Robust Classification
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e. g. corrupted and adversarial samples).
Ranked #40 on
Domain Generalization
on ImageNet-C
MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures
Considering the scarcity of medical data, most datasets in medical image analysis are an order of magnitude smaller than those of natural images.
Geometry Constrained Weakly Supervised Object Localization
The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.
OCT segmentation: Integrating open parametric contour model of the retinal layers and shape constraint to the Mumford-Shah functional
The retinal layer boundary model consists of 9 open parametric contours representing the 9 retinal layers in OCT images.
A binary differential evolution algorithm learning from explored solutions
Although real-coded differential evolution (DE) algorithms can perform well on continuous optimization problems (CoOPs), it is still a challenging task to design an efficient binary-coded DE algorithm.
