149 citations · 183 across the 17 of their papers we have counts for
20 papers
ReactMotion: Generating Reactive Listener Motions from Speaker Utterance
Cheng Luo, Bizhu Wu, Bing Li +5
In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately res…
LabelGS: Label-Aware 3D Gaussian Splatting for 3D Scene Segmentation
Yupeng Zhang, Dezhi Zheng, Ping Lu +8
3D Gaussian Splatting (3DGS) has emerged as a novel explicit representation for 3D scenes, offering both high-fidelity reconstruction and efficient rendering. However, 3DGS lacks 3…
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
Wennuo Yang, Shiling Wu, Yuzhi Zhou +5
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 heal…
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
Xilin He, Jingyu Hu, Qinliang Lin +5
Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks fr…
SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
Xilin He, Cheng Luo, Xiaole Xian +8
Facial expression datasets remain limited in scale due to the subjectivity of annotations and the labor-intensive nature of data collection. This limitation poses a significant cha…
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li, Kun Yuan, Yajing Pei +65
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected da…
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
Zihan Wang, Siyang Song, Cheng Luo +3
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 loc…
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
Qinliang Lin, Cheng Luo, Zenghao Niu +5
Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement a…
REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
Siyang Song, Micol Spitale, Cheng Luo +9
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 re…
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Yuanbo Hou, Siyang Song, Cheng Luo +6
Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches on…
MRecGen: Multimodal Appropriate Reaction Generator
Jiaqi Xu, Cheng Luo, Weicheng Xie +5
Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour. This paper proposes the fir…
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
Siyang Song, Micol Spitale, Cheng Luo +8
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learnin…
ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions
Cheng Luo, Siyang Song, Weicheng Xie +4
In dyadic interaction, predicting the listener's facial reactions is challenging as different reactions could be appropriate in response to the same speaker's behaviour. Previous a…
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
Zihan Wang, Siyang Song, Cheng Luo +4
This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main…
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
Siyang Song, Yuxin Song, Cheng Luo +7
Graph is powerful for representing various types of real-world data. The topology (edges' presence) and edges' features of a graph decides the message passing mechanism among verti…
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation
Junliang Chen, Xiaodong Zhao, Cheng Luo +1
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based i…
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator
Xiangbo Gao, Cheng Luo, Qinliang Lin +5
\noindent Traditional L_p norm-restricted image attack algorithms suffer from poor transferability to black box scenarios and poor robustness to defense algorithms. Recent CNN gene…
Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition
Cheng Luo, Siyang Song, Weicheng Xie +2
The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to spe…
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
Cheng Luo, Qinliang Lin, Weicheng Xie +3
Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations. However, most existing attack methods have inhe…
Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization
Jinheng Xie, Cheng Luo, Xiangping Zhu +3
We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps…