By Professor Roland Goecke

Preprints

Gahalawat M; Bilalpur M; Rojas RF; Cohn JF; Goecke R; Subramanian R, 2025, On the Validity of Head Motion Patterns as Generalisable Depression Biomarkers, http://dx.doi.org/10.48550/arxiv.2505.23427

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://dx.doi.org/10.48550/arxiv.2409.07256

Narayana S; Radwan I; Subramanian R; Goecke R, 2024, Mood as a Contextual Cue for Improved Emotion Inference, http://dx.doi.org/10.48550/arxiv.2402.08413

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://dx.doi.org/10.48550/arxiv.2306.06979

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://dx.doi.org/10.48550/arxiv.2308.02173

Gahalawat M; Rojas RF; Guha T; Subramanian R; Goecke R, 2023, Explainable Depression Detection via Head Motion Patterns, http://dx.doi.org/10.48550/arxiv.2307.12241

Narayana S; Subramanian R; Radwan I; Goecke R, 2023, Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention, http://dx.doi.org/10.48550/arxiv.2303.06632

Madan S; Gahalawat M; Guha T; Goecke R; Subramanian R, 2023, Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics, http://dx.doi.org/10.48550/arxiv.2302.09817

Narayana S; Subramanian R; Radwan I; Goecke R, 2022, To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion, http://dx.doi.org/10.48550/arxiv.2210.00719

Narayana S; Jain S; Katti H; Goecke R; Subramanian R, 2022, Affective Computational Advertising Based on Perceptual Metrics, http://dx.doi.org/10.48550/arxiv.2207.07297

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://dx.doi.org/10.48550/arxiv.2202.12936

Hassanin M; radwan I; Tahtali M; Goecke R, 2022, Resanet: Residual Aggregation Networks for Dense Prediction, http://dx.doi.org/10.2139/ssrn.4212324

Malik H; Dhillon H; Goecke R; Subramanian R, 2020, Characterizing Hirability via Personality and Behavior, http://dx.doi.org/10.48550/arxiv.2006.12041

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://dx.doi.org/10.48550/arxiv.1904.00887

Huang X; Dhall A; Goecke R; Pietikainen M; Zhao G, 2018, A Global Alignment Kernel based Approach for Group-level Happiness Intensity Estimation, http://dx.doi.org/10.48550/arxiv.1809.03313

Dhall A; Kaur A; Goecke R; Gedeon T, 2018, EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction, http://dx.doi.org/10.48550/arxiv.1808.07773

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://dx.doi.org/10.48550/arxiv.1610.03640

Murthy OVR; Goecke R, 2015, Harnessing the Deep Net Object Models for Enhancing Human Action Recognition, http://dx.doi.org/10.48550/arxiv.1512.06498

Radwan I; Dhall A; Goecke R, 2015, Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees, http://dx.doi.org/10.48550/arxiv.1512.01055

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