By Professor Roland Goecke

Preprints

Ghosh S; Cai Z; Dhall A; Kollias D; Goecke R; Gedeon T, 2024, MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing, http://arxiv.org/abs/2409.07256v1

Narayana S; Radwan I; Subramanian R; Goecke R, 2024, Mood as a Contextual Cue for Improved Emotion Inference, http://arxiv.org/abs/2402.08413v1

Parameshwara R; Radwan I; Asthana A; Abbasnejad I; Subramanian R; Goecke R, 2023, Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning, http://arxiv.org/abs/2308.02173v1

Gahalawat M; Rojas RF; Guha T; Subramanian R; Goecke R, 2023, Explainable Depression Detection via Head Motion Patterns, http://arxiv.org/abs/2307.12241v1

Narayana S; Radwan I; Parameshwara R; Abbasnejad I; Asthana A; Subramanian R; Goecke R, 2023, A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference, http://arxiv.org/abs/2306.06979v2

Narayana S; Subramanian R; Radwan I; Goecke R, 2023, Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention, http://arxiv.org/abs/2303.06632v1

Madan S; Gahalawat M; Guha T; Goecke R; Subramanian R, 2023, Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics, http://arxiv.org/abs/2302.09817v2

Narayana S; Subramanian R; Radwan I; Goecke R, 2022, To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion, http://arxiv.org/abs/2210.00719v1

Narayana S; Jain S; Katti H; Goecke R; Subramanian R, 2022, Affective Computational Advertising Based on Perceptual Metrics, http://arxiv.org/abs/2207.07297v1

Parameshwara R; Narayana S; Murugappan M; Subramanian R; Radwan I; Goecke R, 2022, Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals, http://arxiv.org/abs/2202.12936v1

Malik H; Dhillon H; Goecke R; Subramanian R, 2020, Characterizing Hirability via Personality and Behavior, http://arxiv.org/abs/2006.12041v1

Mustafa A; Khan S; Hayat M; Goecke R; Shen J; Shao L, 2019, Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, http://arxiv.org/abs/1904.00887v4

Huang X; Dhall A; Goecke R; Pietikainen M; Zhao G, 2018, A Global Alignment Kernel based Approach for Group-level Happiness Intensity Estimation, http://arxiv.org/abs/1809.03313v1

Dhall A; Kaur A; Goecke R; Gedeon T, 2018, EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction, http://arxiv.org/abs/1808.07773v1

Huang X; Dhall A; Liu X; Zhao G; Shi J; Goecke R; Pietikainen M, 2016, Analyzing the Affect of a Group of People Using Multi-modal Framework, http://arxiv.org/abs/1610.03640v2

Murthy OVR; Goecke R, 2015, Harnessing the Deep Net Object Models for Enhancing Human Action Recognition, http://arxiv.org/abs/1512.06498v2

Radwan I; Dhall A; Goecke R, 2015, Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees, http://arxiv.org/abs/1512.01055v1

Hassanin M; radwan I; Tahtali M; Goecke R, Resanet: Residual Aggregation Networks for Dense Prediction, http://dx.doi.org/10.2139/ssrn.4212324

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