Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses. - Laura Antanas; Martijn van Otterlo; José Oramas M.; Tinne Tuytelaars; Luc De Raedt (ILP, 2010)

Potential benets in the learning process of Ecuadorian Sign Language using a Sign Recognition System. - José Oramas Mogrovejo; Alejandro Manuel Moreno Celleri; Katherine Chiluiza García (e Minds Int. J. Hum. Comput. Interact., 2011)

A Relational Distance-based Framework for Hierarchical Image Understanding. - Laura Antanas; Martijn van Otterlo; José Oramas M.; Tinne Tuytelaars; Luc De Raedt (ICPRAM (2), 2012)

Allocentric Pose Estimation. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (ICCV, 2013)

Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect. - Lieven Billiet; José Oramas M.; McElory Hoffmann; Wannes Meert; Laura Antanas (ICPRAM, 2013)

Scene-driven Cues for Viewpoint Classification for Elongated Object Classes. - José Oramas M.; Tinne Tuytelaars (BMVC, 2014)

Towards cautious collective inference for object verification. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (WACV, 2014)

There are plenty of places like home: Using relational representations in hierarchies for distance-based image understanding. - Laura Antanas; Martijn van Otterlo; José Oramas Mogrovejo; Tinne Tuytelaars; Luc De Raedt (Neurocomputing, 2014)

Modeling video evolution for action recognition. - Basura Fernando; Efstratios Gavves; José Oramas M.; Amir Ghodrati; Tinne Tuytelaars (CVPR, 2015)

Towards sign language recognition based on body parts relations. - Marc Martínez-Camarena; José Oramas M.; Tinne Tuytelaars (ICIP, 2015)

Context-based Reasoning for Object Detection and Object Pose Estimation ; Gebruik van context voor voorwerpherkenning en pose estimatie. - José Oramas Mogrovejo (2015)

Modeling Visual Compatibility through Hierarchical Mid-level Elements. - José Oramas M.; Tinne Tuytelaars (CoRR, 2016)

Reasoning about Body-Parts Relations for Sign Language Recognition. - Marc Martínez-Camarena; José Oramas M.; Mario Montagud Climent; Tinne Tuytelaars (CoRR, 2016)

Recovering hard-to-find object instances by sampling context-based object proposals. - José Oramas M.; Tinne Tuytelaars (Comput. Vis. Image Underst., 2016)

Context-based object viewpoint estimation: A 2D relational approach. - José Oramas M.; Luc De Raedt; Tinne Tuytelaars (Comput. Vis. Image Underst., 2017)

Rank Pooling for Action Recognition. - Basura Fernando; Efstratios Gavves; José Oramas Mogrovejo; Amir Ghodrati; Tinne Tuytelaars (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

From Pixels to Actions: Learning to Drive a Car with Deep Neural Networks. - Jonas Heylen; Seppe Iven; Bert De Brabandere; José Oramas M.; Luc Van Gool; Tinne Tuytelaars (WACV, 2018)

An Analysis of Human-Centered Geolocation. - Kaili Wang; Yu-Hui Huang; José Oramas M.; Luc Van Gool; Tinne Tuytelaars (WACV, 2018)

Integrated unpaired appearance-preserving shape translation across domains. - Kaili Wang; Liqian Ma; José Oramas M.; Luc Van Gool; Tinne Tuytelaars (CoRR, 2018)

A Context Aware Deep Learning Architecture for Object Detection. - Kevin Bardool; Tinne Tuytelaars; José Oramas M. (BNAIC/BENELEARN, 2019)

A Systematic Analysis of a Context Aware Deep Learning Architecture for Object Detection. - Kevin Bardool; Tinne Tuytelaars; José Oramas M. (BNAIC/BENELEARN, 2019)

Interpreting and Explaining Deep Models Visually. - José Oramas M.; Kaili Wang; Tinne Tuytelaars (BNAIC/BENELEARN, 2019)

Towards Object Shape Translation Through Unsupervised Generative Deep Models. - Lies Bollens; Tinne Tuytelaars; José Oramas M. (ICIP, 2019)

Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks. - José Oramas M.; Kaili Wang; Tinne Tuytelaars (ICLR (Poster), 2019)

An Iterative Approach for Multiple Instance Learning Problems. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (CoRR, 2019)

Multiple Exemplars-Based Hallucination for Face Super-Resolution and Editing. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (ACCV (5), 2020)

In Defense of LSTMs for Addressing Multiple Instance Learning Problems. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (ACCV (6), 2020)

AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. - Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas (CIKM, 2020)

Unpaired Image-To-Image Shape Translation Across Fashion Data. - Kaili Wang; Liqian Ma; José Oramas M.; Luc Van Gool; Tinne Tuytelaars (ICIP, 2020)

Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (CoRR, 2020)

Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks? - Roger Granda; Tinne Tuytelaars; José Oramas M. (CoRR, 2020)

MinMaxCAM: Improving object coverage for CAM-based Weakly Supervised Object Localization. - Kaili Wang; José Oramas; Tinne Tuytelaars (BMVC, 2021)

Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices. - Mattias Billast; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (BNAIC/BENELEARN, 2021)

Task Independent Capsule-Based Agents for Deep Q-Learning. - Akash Singh; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (BNAIC/BENELEARN, 2021)

Towards Human-Understandable Visual Explanations: Imperceptible High-frequency Cues Can Better Be Removed. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (CoRR, 2021)

MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization. - Kaili Wang; José Oramas M.; Tinne Tuytelaars (CoRR, 2021)

AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence. - Adrien Bibal; Tassadit Bouadi; Benoît Frénay; Luis Galárraga; José Oramas (CIKM, 2022)

Object Detection To Enable Autonomous Vessels On European Inland Waterways. - Mattias Billast; Robin Janssens; Astrid Vanneste; Simon Vanneste; Olivier Vasseur; Ali Anwar 002; Kevin Mets; Tom De Schepper; José Oramas; Steven Latré; Peter Hellinckx (IECON, 2022)

Deep Set Conditioned Latent Representations for Action Recognition. - Akash Singh; Tom De Schepper; Kevin Mets; Peter Hellinckx; José Oramas; Steven Latré (VISIGRAPP (5: VISAPP), 2022)

Interpreting Convolutional Neural Networks by Explaining Their Predictions. - Toon Meynen; Hamed Behzadi-Khormouji; José Oramas (ICIP, 2023)

Automated Virtual Reduction of Displaced Distal Radius Fractures. - Jana Osstyn; Femke Danckaers; Annemieke Van Haver; José Oramas; Matthias Vanhees; Jan Sijbers (ISBI, 2023)

Analyzing the Explanation and Interpretation Potential of Matrix Capsule Networks. - Andrei Bondarenko; Saja Tawalbeh; José Oramas (PKDD/ECML Workshops (1), 2023)

Human Motion Prediction on the IKEA-ASM Dataset. - Mattias Billast; Kevin Mets; Tom De Schepper; José Oramas; Steven Latré (VISIGRAPP (5: VISAPP), 2023)

A Protocol for Evaluating Model Interpretation Methods from Visual Explanations. - Hamed Behzadi Khormuji; José Oramas (WACV, 2023)

A Contrastive Learning Method for Multi-Label Predictors on Hyperspectral Images. - Salma Haidar; José Oramas (WHISPERS, 2023)

On The Coherence of Quantitative Evaluation of Visual Expalantion. - Benjamin Vandersmissen; José Oramas (CoRR, 2023)

Considering Layerwise Importance in the Lottery Ticket Hypothesis. - Benjamin Vandersmissen; José Oramas (CoRR, 2023)

FICNN: A Framework for the Interpretation of Deep Convolutional Neural Networks. - Hamed Behzadi-Khormouji; José Oramas (CoRR, 2023)

Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images. - Salma Haidar; José Oramas (Remote. Sens., 2023)

Deep Learning Model Compression for Resource Efficient Activity Recognition on Edge Devices: A Case Study. - Dieter Balemans; Benjamin Vandersmissen; Jan Steckel; Siegfried Mercelis; Philippe Reiter; José Oramas (VISIGRAPP (4): VISAPP, 2024)

Recognizing Actions in High-Resolution Low-Framerate Videos: A Feasibility Study in the Construction Sector. - Benjamin Vandersmissen; Arian Sabaghi; Philippe Reiter; José Oramas (VISIGRAPP (4): VISAPP, 2024)

Smooth InfoMax - Towards easier Post-Hoc interpretability. - Fabian Denoodt; Bart de Boer; José Oramas (CoRR, 2024)

Deep Model Interpretation with Limited Data : A Coreset-based Approach. - Hamed Behzadi-Khormouji; José Oramas (CoRR, 2024)

Enhancing Hyperspectral Image Prediction with Contrastive Learning in Low-Label Regime. - Salma Haidar; José Oramas (CoRR, 2024)

On the coherency of quantitative evaluation of visual explanations. - Benjamin Vandersmissen; José Oramas (Comput. Vis. Image Underst., 2024)

The Trifecta: Three simple techniques for training deeper Forward-Forward networks. - Thomas Dooms; Ing Jyh Tsang; José Oramas (Trans. Mach. Learn. Res., 2024)

SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake Detection. - Yuqing Yang; Boris Joukovsky; José Oramas Mogrovejo; Tinne Tuytelaars; Nikos Deligiannis (ACM Trans. Multim. Comput. Commun. Appl., 2024)

Bilinear MLPs enable weight-based mechanistic interpretability. - Michael T. Pearce; Thomas Dooms; Alice Rigg; José Oramas; Lee Sharkey (ICLR, 2025)

Improving Neural Network Accuracy by Concurrently Training with a Twin Network. - Benjamin Vandersmissen; Lucas Deckers; José Oramas M. (ICLR, 2025)

Quantifying Generative Stability: Mode Collapse Entropy Score for Mode Diversity Evaluation. - Jens Duym; José Oramas; Ali Anwar 002 (WACV (Workshops), 2025)

Label-Efficient Learning for Radio Frequency Fingerprint Identification. - Thayheng Nhem; Fabian Denoodt; Maarten Weyn; Michaël Peeters; José Oramas; Rafael Berkvens (WCNC, 2025)

Towards the characterization of representations learned via capsule-based network architectures. - Saja AL-Tawalbeh; José Oramas (Neurocomputing, 2025)

Efficient Post-Hoc Uncertainty Calibration via Variance-Based Smoothing. - Fabian Denoodt; José Oramas (CoRR, 2025)

Compositionality Unlocks Deep Interpretable Models. - Thomas Dooms; Ward Gauderis; Geraint A. Wiggins; José Oramas (CoRR, 2025)

A beginner's approach to deep learning applied to VS and MD techniques. - Stijn Dhondt; José Oramas; Hans De Winter (J. Cheminformatics, 2025)