Continuous Learning Based Novelty Aware Emotion Recognition System
Current works in human emotion recognition follow the traditional closed learning approach governed by rigid rules without any consideration of novelty. Classification models are trained on some collected datasets and expected to have the same data distribution in the real-world deployment. Due to the fluid and constantly changing nature of the world we live in, it is possible to have unexpected and novel sample distribution which can lead the model to fail. Hence, in this work, we propose a continuous learning based approach to deal with novelty in the automatic emotion recognition task.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SAFER: Situation Aware Facial Emotion Recognition
In this paper, we present SAFER, a novel system for emotion recognition from facial expressions. It employs state-of-the-art deep learning techniques to extract various features from facial images and incorporates contex…
Emotion RecognitionFacial Emotion RecognitionInsideOut: An EfficientNetV2-S Based Deep Learning Framework for Robust Multi-Class Facial Emotion Recognition
Facial Emotion Recognition (FER) is a key task in affective computing, enabling applications in human-computer interaction, e-learning, healthcare, and safety systems. Despite advances in deep learning, FER remains chall…
Facial Emotion RecognitionTransfer LearningData AugmentationContinuous Adversarial Text Representation Learning for Affective Recognition
While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel f…
Adversarial TextContrastive LearningEmotion ClassificationRepresentation LearningSelf context-aware emotion perception on human-robot interaction
Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recogniti…
Emotion RecognitionMAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition
Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding. However, deploying such systems in open-world environments requires detecting novel activitie…
Egocentric Activity RecognitionContinual LearningActivity Detection