Search Results for author:
Found 28 papers, 11 papers with code
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis
Our model also achieves state-of-the-art performance on a widely used sarcasm dataset.
Multimodal Fusion with Pre-Trained Model Features in Affective Behaviour Analysis In-the-wild
Multimodal fusion is a significant method for most multimodal tasks.
Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
To address data scarcity, this paper proposes a new data collection pipeline.
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition
Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in recent years for its critical role in creating emotion-ware intelligent machines.
Ranked #3 on
Dynamic Facial Expression Recognition
on MAFW
Contrastive Learning
Dynamic Facial Expression Recognition
+3
SVFAP: Self-supervised Video Facial Affect Perceiver
Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction.
Ranked #3 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Emotion Recognition
+2
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Realistic Incomplete Data Scenarios
Multimodal emotion recognition (MER) in practical scenarios presents a significant challenge due to the presence of incomplete data, such as missing or noisy data.
MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines.
Ranked #2 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Facial Expression Recognition
MFAS: Emotion Recognition through Multiple Perspectives Fusion Architecture Search Emulating Human Cognition
Additionally, considering that humans adjust their perception of emotional words in textual semantic based on certain cues present in speech, we design a novel search space and search for the optimal fusion strategy for the two types of information.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
Pseudo Labels Regularization for Imbalanced Partial-Label Learning
Partial-label learning (PLL) is an important branch of weakly supervised learning where the single ground truth resides in a set of candidate labels, while the research rarely considers the label imbalance.
IRNet: Iterative Refinement Network for Noisy Partial Label Learning
In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set.
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate.
Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis
With the proliferation of user-generated online videos, Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently.
Two-Aspect Information Fusion Model For ABAW4 Multi-task Challenge
In this paper, we propose the solution to the Multi-Task Learning (MTL) Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition
Speech emotion recognition (SER) is an important research topic in human-computer interaction.
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation
To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
Cross Modification Attention Based Deliberation Model for Image Captioning
The conventional encoder-decoder framework for image captioning generally adopts a single-pass decoding process, which predicts the target descriptive sentence word by word in temporal order.
BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition
Emotion recognition remains a complex task due to speaker variations and low-resource training samples.
Ranked #1 on
Speech Emotion Recognition
on IEMOCAP
(using extra training data)
Conversational Emotion Analysis via Attention Mechanisms
Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis.
Domain adversarial learning for emotion recognition
The secondary task is to learn a common representation where speaker identities can not be distinguished.
Unsupervised Representation Learning with Future Observation Prediction for Speech Emotion Recognition
Prior works on speech emotion recognition utilize various unsupervised learning approaches to deal with low-resource samples.
Expression Analysis Based on Face Regions in Read-world Conditions
To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis.
Speech Emotion Recognition via Contrastive Loss under Siamese Networks
It outperforms the baseline system that is optimized without the contrastive loss function with 1. 14% and 2. 55% in the weighted accuracy and the unweighted accuracy, respectively.
Improving speech emotion recognition via Transformer-based Predictive Coding through transfer learning
I have submitted a new version to arXiv:1910. 13806.
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
We test our method in the EmotiW 2018 challenge and we gain promising results.
