arXiv AI

Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks

arXiv:2607. 20820v1 Announce Type: new Abstract: Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive.

arXiv Machine Learning

Jun 30

Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions

arXiv:2606. 28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication.

By Huakun Liu, Miao Cheng, Xin Wei, Felix Dollack, Victor Schneider, Hideaki Uchiyama, Chia-huei Tseng, Yoshifumi Kitamura, Monica Perusquia-Hernandez

llms robotics safety

Hugging Face Trending Papers

Jun 30

Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition

In this paper, we present the solution developed by our team, XInsight Lab, which achieved first place in Track 3 of the 4th EI-MIGA-IJCAI Challenge with a test accuracy of 0. 76923.

llms multimodal benchmarks

arXiv Machine Learning

Aug 11

Sparse Attention to Emotion: Efficient Facial Emotion Recognition via Token Reduction

arXiv:2608. 08873v1 Announce Type: cross Abstract: Facial Emotion Recognition (FER) is an important task that has significant implications across various fields such as biometrics, health, and human-computer interaction.

By Aya Manel Zitouni, Aicha Zenakhri, Karim Haroun, Larbi Boubchir

llms computer-vision

arXiv AI

Jul 21

Explainable Lightweight Compact Deep Models for Speech Emotion Recognition

arXiv:2607. 16803v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) is an important component in a wide range of human-centered applications, including healthcare, customer service, and human-omputer interaction.

By Nelly Elsayed

safety

arXiv AI

Jul 15

HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition

arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.

By Aleksei Bakin, Andrey V. Savchenko

fine-tuning multimodal safety

arXiv Machine Learning

Jul 20

An Empirical Study of Handcrafted Feature Learning and Convolutional Neural Networks for Facial Expression Recognition

arXiv:2607. 15288v1 Announce Type: cross Abstract: Facial expression recognition is an important computer vision task with applications in human--computer interaction, mental health monitoring, driver alert systems, and behavioral analysis.

By Chethiya Galkaduwa

computer-vision

arXiv AI

Jul 22

Why Do Vision Language Models Struggle To Recognize Human Emotions?

arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.

By Madhav Agarwal, Sotirios A. Tsaftaris, Laura Sevilla-Lara, Steven McDonagh

llms multimodal safety

arXiv AI

Jul 21

Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions

arXiv:2607. 16196v1 Announce Type: new Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect.

By Aleksandrs Vali\v{s}evskis, Aleksandrs Okss, Inese T\=i\c{g}ere, Aleksejs Kata\v{s}evs, Dina Bethere, Anete Hofmane, Airisa \v{S}teinberga, Und\=ine Gavri\c{l}enko, Santa Me\c{l}\c{k}e, Lucie Matou\v{s}kov\'a

rag efficiency safety

arXiv Machine Learning

Jun 25

Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.

By Felipe Moreno, Sharifa Alghowinem, Hae Won Park, Cynthia Breazeal

fine-tuning benchmarks safety

arXiv AI

1d ago

Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection

arXiv:2608. 14675v1 Announce Type: cross Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven.

By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski

reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 10

DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition

arXiv:2602. 06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity, subject variability, and limited interpretability.

By S M Rakib UI Karim, Diponkor Bala, Wenyi Lu, Rownak Ara Rasul, Sean Goggins

llms safety

arXiv Machine Learning

Jun 26

State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models

arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.

By Andrei Velichko, Mehmet Tahir Huyut

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