Papers Dynamic Facial Expression Recognition
“Dynamic Facial Expression Recognition” 태그가 달린 논문 42편 · 필터 해제
Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation
Dynamic facial expression recognition (DFER) is a task that estimates emotions from facial expression video sequences. For practical applications, accurately recognizing ambiguous facial expressions -- frequently encount…
Data AugmentationDynamic Facial Expression RecognitionFacial Expression RecognitionVAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection
Audiovisual emotion recognition (AVER) aims to infer human emotions from nonverbal visual-audio (VA) cues, offering modality-complementary and language-agnostic advantages. However, AVER remains challenging due to the in…
Contrastive LearningDynamic Facial Expression RecognitionEmotion RecognitionRepresentation Learning+2PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition
Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high complexity, and poor alignment between tex…
Dynamic Facial Expression RecognitionFacial Expression Recognitionparameter-efficient fine-tuningPrompt LearningFRU-Adapter: Frame Recalibration Unit Adapter for Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is one of the most important challenges in computer vision, as it plays a crucial role in human–computer interaction. Re cently, adapter-based approaches have been introduc…
Dynamic Facial Expression RecognitionFacial Expression RecognitionMIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambiguous facial expressions, which often ap…
Data AugmentationDynamic Facial Expression RecognitionFacial Expression RecognitionRobust Dynamic Facial Expression Recognition
The study of Dynamic Facial Expression Recognition (DFER) is a nascent field of research that involves the automated recognition of facial expressions in video data. Although existing research has primarily focused on le…
Dynamic Facial Expression RecognitionFacial Expression RecognitionD2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.
The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial …
Dynamic Facial Expression RecognitionFacial Expression RecognitionMTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
This paper expands the cascaded network branch of the autoencoder-based multi-task learning (MTL) framework for dynamic facial expression recognition, namely Multi-Task Cascaded Autoencoder for Dynamic Facial Expression …
Dynamic Facial Expression RecognitionFace DetectionFacial Emotion RecognitionFacial Expression Recognition+2Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions a…
DisentanglementDynamic Facial Expression RecognitionFacial Expression RecognitionEmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted Displays
Emotion recognition promotes the evaluation and enhancement of Virtual Reality (VR) experiences by providing emotional feedback and enabling advanced personalization. However, facial expressions are rarely used to recogn…
Dynamic Facial Expression RecognitionEmotion RecognitionFacial Expression RecognitionFacial Expression Recognition (FER)Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data
Dynamic facial expression recognition (DFER) infers emotions from the temporal evolution of expressions, unlike static facial expression recognition (SFER), which relies solely on a single snapshot. This temporal analysi…
Dynamic Facial Expression RecognitionFacial Expression RecognitionMulti-Task LearningFE-Adapter: Adapting Image-based Emotion Classifiers to Videos
Utilizing large pre-trained models for specific tasks has yielded impressive results. However, fully fine-tuning these increasingly large models is becoming prohibitively resource-intensive. This has led to a focus on mo…
Dynamic Facial Expression RecognitionEmotion RecognitionTransfer LearningVideo Emotion Recognition+1All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by the process through wh…
AllDynamic Facial Expression RecognitionEmotion ClassificationFacial Expression RecognitionFineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs
Dynamic Facial Expression Recognition (DFER) is crucial for understanding human behavior. However, current methods exhibit limited performance mainly due to the scarcity of high-quality data, the insufficient utilization…
Dynamic Facial Expression RecognitionFacial Expression RecognitionLanguage ModellingLarge Language Model+1D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by trainin…
Dynamic Facial Expression RecognitionFacial Expression RecognitionOUS: Scene-Guided Dynamic Facial Expression Recognition
Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically in…
Dynamic Facial Expression RecognitionFacial Expression RecognitionMMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Dynamic Facial Expression Recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving robustness towards in-the-…
cross-modal alignmentDynamic Facial Expression RecognitionEmotion RecognitionFacial Expression Recognition+2MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition
Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we …
Action RecognitionAttributeDynamic Facial Expression RecognitionFacial Expression Recognition+1A$^{3}$lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP
The performance of CLIP in dynamic facial expression recognition (DFER) task doesn't yield exceptional results as observed in other CLIP-based classification tasks. While CLIP's primary objective is to achieve alignment …
Dynamic Facial Expression RecognitionFacial Expression RecognitionHiCMAE: 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. Previous efforts in this area are dominated by the supervised…
Contrastive LearningDynamic Facial Expression RecognitionEmotion RecognitionRepresentation Learning+1