Optimal feature selection for subspace image matching. - Gemma Roig; Xavier Boix; Fernando De la Torre (ICCV Workshops, 2009)

Harmony potentials for joint classification and segmentation. - Josep M. Gonfaus; Xavier Boix Bosch; Joost van de Weijer 001; Andrew D. Bagdanov; Joan Serrat Gual; Jordi Gonzàlez 001 (CVPR, 2010)

Hierarchical CRF with product label spaces for parts-based models. - Gemma Roig; Xavier Boix Bosch; Fernando De la Torre; Joan Serrat Gual; Carles Vilella (FG, 2011)

Are spatial and global constraints really necessary for segmentation? - Aurélien Lucchi; Yunpeng Li 002; Xavier Boix Bosch; Kevin Smith 001; Pascal Fua (ICCV, 2011)

Conditional Random Fields for multi-camera object detection. - Gemma Roig; Xavier Boix; Horesh Ben Shitrit; Pascal Fua (ICCV, 2011)

SEEDS: Superpixels Extracted via Energy-Driven Sampling. - Michael Van den Bergh; Xavier Boix; Gemma Roig; Benjamin de Capitani; Luc Van Gool (ECCV (7), 2012)

Nested Sparse Quantization for Efficient Feature Coding. - Xavier Boix; Gemma Roig; Christian Leistner; Luc Van Gool (ECCV (2), 2012)

On-line semantic perception using uncertainty. - Roderick de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; Kolja Kühnlenz (IROS, 2012)

Harmony Potentials - Fusing Global and Local Scale for Semantic Image Segmentation. - Xavier Boix; Josep M. Gonfaus; Joost van de Weijer 001; Andrew D. Bagdanov; Joan Serrat Gual; Jordi Gonzàlez 001 (Int. J. Comput. Vis., 2012)

Sparse Quantization for Patch Description. - Xavier Boix; Michael Gygli; Gemma Roig; Luc Van Gool (CVPR, 2013)

Online Video SEEDS for Temporal Window Objectness. - Michael Van den Bergh; Gemma Roig; Xavier Boix; Santiago Manen; Luc Van Gool (ICCV, 2013)

Active MAP Inference in CRFs for Efficient Semantic Segmentation. - Gemma Roig; Xavier Boix; Roderick de Nijs; Sebastian Ramos; Kolja Kühnlenz; Luc Van Gool (ICCV, 2013)

Random Binary Mappings for Kernel Learning and Efficient SVM. - Gemma Roig; Xavier Boix; Luc Van Gool (CoRR, 2013)

Reconstruction of Inextensible Surfaces on a Budget via Bootstrapping. - Alex Locher; Lennart Elsen; Xavier Boix; Luc Van Gool (3DV, 2014)

Self-Adaptable Templates for Feature Coding. - Xavier Boix; Gemma Roig; Salomon Diether; Luc Van Gool (NIPS, 2014)

Comment on "Ensemble Projection for Semi-supervised Image Classification". - Xavier Boix; Gemma Roig; Luc Van Gool (CoRR, 2014)

Saliency Prediction with Active Semantic Segmentation. - Ming Jiang 019; Xavier Boix; Juan Xu; Gemma Roig; Luc Van Gool; Qi Zhao 001 (BMVC, 2015)

SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks. - Xun Huang; Chengyao Shen; Xavier Boix; Qi Zhao 001 (ICCV, 2015)

Foveation-based Mechanisms Alleviate Adversarial Examples. - Yan Luo 002; Xavier Boix; Gemma Roig; Tomaso A. Poggio; Qi Zhao 001 (CoRR, 2015)

SEEDS: Superpixels Extracted Via Energy-Driven Sampling. - Michael Van den Bergh; Xavier Boix; Gemma Roig; Luc Van Gool (Int. J. Comput. Vis., 2015)

Computational commonalities of image understanding algorithms. - Xavier Boix (2015)

Predicting When Saliency Maps are Accurate and Eye Fixations Consistent. - Anna Volokitin; Michael Gygli; Xavier Boix (CVPR, 2016)

Herding Generalizes Diverse M -Best Solutions. - Ece Ozkan; Gemma Roig; Orcun Goksel; Xavier Boix (CoRR, 2016)

Learning to Predict Sequences of Human Visual Fixations. - Ming Jiang 019; Xavier Boix; Gemma Roig; Juan Xu; Luc Van Gool; Qi Zhao 001 (IEEE Trans. Neural Networks Learn. Syst., 2016)

Active Video Summarization: Customized Summaries via On-line Interaction with the User. - Ana Garcia del Molino; Xavier Boix; Joo-Hwee Lim; Ah-Hwee Tan (AAAI, 2017)

Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. - Francis X. Chen; Gemma Roig; Leyla Isik; Xavier Boix; Tomaso A. Poggio (AAAI Spring Symposia, 2017)

Predicting When Eye Fixations Are Consistent. - Anna Volokitin; Michael Gygli; Xavier Boix (AAAI Spring Symposia, 2017)

Reports on the 2017 AAAI Spring Symposium Series. - Jeannette Bohg; Xavier Boix; Nancy Chang; Elizabeth F. Churchill; Vivian Chu; Fei Fang 001; Jerome Feldman; Avelino J. Gonzalez; Takashi Kido; William F. Lawless; José L. Montaña; Santiago Ontañón; et al. (AI Mag., 2017)

Theory of Deep Learning III: explaining the non-overfitting puzzle. - Tomaso A. Poggio; Kenji Kawaguchi; Qianli Liao; Brando Miranda; Lorenzo Rosasco; Xavier Boix; Jack Hidary; Hrushikesh N. Mhaskar (CoRR, 2018)

Theory IIIb: Generalization in Deep Networks. - Tomaso A. Poggio; Qianli Liao; Brando Miranda; Andrzej Banburski; Xavier Boix; Jack Hidary (CoRR, 2018)

Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. - Sanjana Srivastava; Guy Ben-Yosef; Xavier Boix (ICLR (Poster), 2019)

Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. - Stephen Casper; Xavier Boix; Vanessa D'Amario; Ling Guo; Martin Schrimpf; Kasper Vinken; Gabriel Kreiman (CoRR, 2019)

Is Robustness To Transformations Driven by Invariant Neural Representations? - Syed Suleman Abbas Zaidi; Xavier Boix; Neeraj Prasad; Sharon Gilad-Gutnick; Shlomit Ben-Ami; Pawan Sinha (CoRR, 2020)

On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. - Spandan Madan; Timothy Henry; Jamell Dozier; Helen Ho; Nishchal Bhandari; Tomotake Sasaki; Frédo Durand; Hanspeter Pfister; Xavier Boix (CoRR, 2020)

Frivolous Units: Wider Networks Are Not Really That Wide. - Stephen Casper; Xavier Boix; Vanessa D'Amario; Ling Guo; Martin Schrimpf; Kasper Vinken; Gabriel Kreiman (AAAI, 2021)

How Modular should Neural Module Networks Be for Systematic Generalization? - Vanessa D'Amario; Tomotake Sasaki; Xavier Boix (NeurIPS, 2021)

Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers. - Spandan Madan; Tomotake Sasaki; Tzu-Mao Li; Xavier Boix; Hanspeter Pfister (CoRR, 2021)

The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions. - Vanessa D'Amario; Sanjana Srivastava; Tomotake Sasaki; Xavier Boix (CoRR, 2021)

To Which Out-Of-Distribution Object Orientations Are DNNs Capable of Generalizing? - Avi Cooper; Xavier Boix; Daniel Harari; Spandan Madan; Hanspeter Pfister; Tomotake Sasaki; Pawan Sinha (CoRR, 2021)

Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss. - Akira Sakai; Taro Sunagawa; Spandan Madan; Kanata Suzuki; Takashi Katoh; Hiromichi Kobashi; Hanspeter Pfister; Pawan Sinha; Xavier Boix; Tomotake Sasaki (CoRR, 2021)

Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections. - Shobhita Sundaram; Darius Sinha; Matthew Groth; Tomotake Sasaki; Xavier Boix (CoRR, 2021)

A Robust Optimization Approach to Deep Learning. - Dimitris Bertsimas; Xavier Boix; Kimberly Villalobos Carballo; Dick den Hertog (CoRR, 2021)

Do Neural Networks for Segmentation Understand Insideness? - Kimberly Villalobos; Vilim Stih; Amineh Ahmadinejad; Shobhita Sundaram; Jamell Dozier; Andrew Francl; Frederico Azevedo; Tomotake Sasaki; Xavier Boix (Neural Comput., 2021)

The Data Efficiency of Deep Learning Is Degraded by Unnecessary Input Dimensions. - Vanessa D'Amario; Sanjana Srivastava; Tomotake Sasaki; Xavier Boix (Frontiers Comput. Neurosci., 2022)

When and how convolutional neural networks generalize to out-of-distribution category-viewpoint combinations. - Spandan Madan; Timothy Henry; Jamell Dozier; Helen Ho; Nishchal Bhandari; Tomotake Sasaki; Frédo Durand; Hanspeter Pfister; Xavier Boix (Nat. Mach. Intell., 2022)

Three approaches to facilitate invariant neurons and generalization to out-of-distribution orientations and illuminations. - Akira Sakai; Taro Sunagawa; Spandan Madan; Kanata Suzuki; Takashi Katoh; Hiromichi Kobashi; Hanspeter Pfister; Pawan Sinha; Xavier Boix; Tomotake Sasaki (Neural Networks, 2022)

Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts. - Anirban Sarkar; Matthew Groth; Ian Mason; Tomotake Sasaki; Xavier Boix (CoRR, 2023)

Modularity Trumps Invariance for Compositional Robustness. - Ian Mason; Anirban Sarkar; Tomotake Sasaki; Xavier Boix (CoRR, 2023)

Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysis. - Ece Ozkan; Xavier Boix (CoRR, 2023)

Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations? - Hojin Jang; Syed Suleman Abbas Zaidi; Xavier Boix; Neeraj Prasad; Sharon Gilad-Gutnick; Shlomit Ben-Ami; Pawan Sinha (Neural Comput., 2023)

Configural processing as an optimized strategy for robust object recognition in neural networks. - Hojin Jang; Pawan Sinha; Xavier Boix (CoRR, 2024)

Rethinking VLMs and LLMs for Image Classification. - Avi Cooper; Keizo Kato; Chia-Hsien Shih; Hiroaki Yamane; Kasper Vinken; Kentaro Takemoto; Taro Sunagawa; Hao-Wei Yeh; Jin Yamanaka; Ian Mason; Xavier Boix (CoRR, 2024)

D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. - Amir Rahimi; Vanessa D'Amario; Moyuru Yamada; Kentaro Takemoto; Tomotake Sasaki; Xavier Boix (Trans. Mach. Learn. Res., 2024)

Transformer Module Networks for Systematic Generalization in Visual Question Answering. - Moyuru Yamada; Vanessa D'Amario; Kentaro Takemoto; Xavier Boix; Tomotake Sasaki (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations. - Avi Cooper; Daniel Harari; Tomotake Sasaki; Spandan Madan; Hanspeter Pfister; Pawan Sinha; Xavier Boix (Trans. Mach. Learn. Res., 2025)

In-distribution adversarial attacks on object recognition models using gradient-free search. - Spandan Madan; Tomotake Sasaki; Hanspeter Pfister; Tzu-Mao Li; Xavier Boix (Trans. Mach. Learn. Res., 2025)

Task Arithmetic Through The Lens Of One-Shot Federated Learning. - Zhixu Tao; Ian Mason; Sanjeev R. Kulkarni; Xavier Boix (Trans. Mach. Learn. Res., 2025)

Merge to Mix: Mixing Datasets via Model Merging. - Zhixu Silvia Tao; Kasper Vinken; Hao-Wei Yeh; Avi Cooper; Xavier Boix (CoRR, 2025)