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Found 26 papers, 17 papers with code
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.
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.
REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour.
Multi-level graph learning for audio event classification and human-perceived annoyance rating prediction
Specifically, this paper proposes a lightweight multi-level graph learning (MLGL) based on local and global semantic graphs to simultaneously perform audio event classification (AEC) and human annoyance rating prediction (ARP).
Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification
The results show the feasibility of recognizing diverse acoustic scenes based on the audio event-relational graph.
Acoustic Scene Classification
Graph Representation Learning
+1
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.
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions.
ReactFace: Multiple Appropriate Facial Reaction Generation in Dyadic Interactions
ReactFace generates multiple different but appropriate photo-realistic human facial reactions by (i) learning an appropriate facial reaction distribution representing multiple appropriate facial reactions; and (ii) synchronizing the generated facial reactions with the speaker's verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences.
Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generation
This means that we approach this problem by considering the generation of a distribution of the listener's appropriate facial reactions instead of multiple different appropriate facial reactions, i. e., 'many' appropriate facial reaction labels are summarised as 'one' distribution label during training.
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).
Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?
However, none attempted to automatically generate multiple appropriate reactions in the context of dyadic interactions and evaluate the appropriateness of those reactions using objective measures.
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.
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
Ranked #3 on
Medical Image Registration
on OASIS
(val dsc metric)
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.
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification
Experiments on a polyphonic acoustic scene dataset show that the proposed ERGL achieves competitive performance on ASC by using only a limited number of embeddings of audio events without any data augmentations.
Acoustic Scene Classification
Graph Representation Learning
+1
An Open-source Benchmark of Deep Learning Models for Audio-visual Apparent and Self-reported Personality Recognition
Personality determines a wide variety of human daily and working behaviours, and is crucial for understanding human internal and external states.
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.
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 #3 on
Facial Action Unit Detection
on DISFA
Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation
The short-term depressive behaviour modelling stage first deep learns depression-related facial behavioural features from multiple short temporal scales, where a Depression Feature Enhancement (DFE) module is proposed to enhance the depression-related clues for all temporal scales and remove non-depression noises.
Inferring User Facial Affect in Work-like Settings
Unlike the six basic emotions of happiness, sadness, fear, anger, disgust and surprise, modelling and predicting dimensional affect in terms of valence (positivity - negativity) and arousal (intensity) has proven to be more flexible, applicable and useful for naturalistic and real-world settings.
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition
This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.
EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of machine learning and multimedia processing methods of automatic chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
Inferring Dynamic Representations of Facial Actions from a Still Image
We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
