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Xavier Boix
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- affiliation: ETH Zurich, Switzerland
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2020 – today
- 2024
- [j10]Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix:
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. Trans. Mach. Learn. Res. 2024 (2024) - [i25]Hojin Jang, Pawan Sinha, Xavier Boix:
Configural processing as an optimized strategy for robust object recognition in neural networks. CoRR abs/2407.19072 (2024) - 2023
- [j9]Hojin Jang
, Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad, Sharon Gilad-Gutnick, Shlomit Ben-Ami, Pawan Sinha:
Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations? Neural Comput. 35(12): 1910-1937 (2023) - [i24]Anirban Sarkar, Matthew Groth, Ian Mason, Tomotake Sasaki, Xavier Boix:
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts. CoRR abs/2303.11912 (2023) - [i23]Ian Mason, Anirban Sarkar, Tomotake Sasaki, Xavier Boix:
Modularity Trumps Invariance for Compositional Robustness. CoRR abs/2306.09005 (2023) - [i22]Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix:
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. CoRR abs/2309.08798 (2023) - [i21]Ece Ozkan, Xavier Boix:
Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysis. CoRR abs/2310.06737 (2023) - 2022
- [j8]Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki
The Data Efficiency of Deep Learning Is Degraded by Unnecessary Input Dimensions. Frontiers Comput. Neurosci. 16: 760085 (2022) - [j7]Spandan Madan
When and how convolutional neural networks generalize to out-of-distribution category-viewpoint combinations. Nat. Mach. Intell. 4(2): 146-153 (2022) - [j6]Akira Sakai
Three approaches to facilitate invariant neurons and generalization to out-of-distribution orientations and illuminations. Neural Networks 155: 119-143 (2022) - [i20]Kimberly Villalobos, Vilim Stih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki, Xavier Boix:
Do Neural Networks for Segmentation Understand Insideness? CoRR abs/2201.10664 (2022) - [i19]Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto, Xavier Boix, Tomotake Sasaki:
Transformer Module Networks for Systematic Generalization in Visual Question Answering. CoRR abs/2201.11316 (2022) - 2021
- [j5]Kimberly Villalobos, Vilim Stih, Amineh Ahmadinejad, Shobhita Sundaram, Jamell Dozier, Andrew Francl, Frederico Azevedo, Tomotake Sasaki
Do Neural Networks for Segmentation Understand Insideness? Neural Comput. 33(9): 2511-2549 (2021) - [c23]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman:
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021: 6921-6929 - [c22]Vanessa D'Amario, Tomotake Sasaki, Xavier Boix:
How Modular should Neural Module Networks Be for Systematic Generalization? NeurIPS 2021: 23374-23385 - [i18]Vanessa D'Amario, Tomotake Sasaki, Xavier Boix:
How Modular Should Neural Module Networks Be for Systematic Generalization? CoRR abs/2106.08170 (2021) - [i17]Spandan Madan, Tomotake Sasaki, Tzu-Mao Li, Xavier Boix, Hanspeter Pfister:
Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers. CoRR abs/2106.16198 (2021) - [i16]Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki, Xavier Boix:
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions. CoRR abs/2107.06409 (2021) - [i15]Avi Cooper, Xavier Boix, Daniel Harari, Spandan Madan, Hanspeter Pfister, Tomotake Sasaki, Pawan Sinha:
To Which Out-Of-Distribution Object Orientations Are DNNs Capable of Generalizing? CoRR abs/2109.13445 (2021) - [i14]Akira Sakai, Taro Sunagawa, Spandan Madan, Kanata Suzuki, Takashi Katoh, Hiromichi Kobashi, Hanspeter Pfister, Pawan Sinha, Xavier Boix, Tomotake Sasaki:
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations: late-stopping, tuning batch normalization and invariance loss. CoRR abs/2111.00131 (2021) - [i13]Shobhita Sundaram, Darius Sinha, Matthew Groth, Tomotake Sasaki, Xavier Boix:
Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections. CoRR abs/2112.04162 (2021) - [i12]Dimitris Bertsimas, Xavier Boix, Kimberly Villalobos Carballo, Dick den Hertog:
A Robust Optimization Approach to Deep Learning. CoRR abs/2112.09279 (2021) - 2020
- [i11]Syed Suleman Abbas Zaidi, Xavier Boix, Neeraj Prasad, Sharon Gilad-Gutnick, Shlomit Ben-Ami, Pawan Sinha:
Is Robustness To Transformations Driven by Invariant Neural Representations? CoRR abs/2007.00112 (2020) - [i10]Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, Xavier Boix:
On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. CoRR abs/2007.08032 (2020)
2010 – 2019
- 2019
- [c21]Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix:
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. ICLR (Poster) 2019 - [i9]Sanjana Srivastava, Guy Ben-Yosef
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. CoRR abs/1902.03227 (2019) - [i8]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR abs/1912.04783 (2019) - 2018
- [i7]Tomaso A. Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh N. Mhaskar:
Theory of Deep Learning III: explaining the non-overfitting puzzle. CoRR abs/1801.00173 (2018) - [i6]Tomaso A. Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary:
Theory IIIb: Generalization in Deep Networks. CoRR abs/1806.11379 (2018) - 2017
- [j4]Jeannette Bohg
Reports on the 2017 AAAI Spring Symposium Series. AI Mag. 38(4): 99-106 (2017) - [c20]Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan:
Active Video Summarization: Customized Summaries via On-line Interaction with the User. AAAI 2017: 4046-4052 - [c19]Francis X. Chen, Gemma Roig, Leyla Isik, Xavier Boix, Tomaso A. Poggio:
Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. AAAI Spring Symposia 2017 - [c18]Ana Garcia del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan:
On Active Video Summarization: Customized Summaries via On-Line Interaction with the User. AAAI Spring Symposia 2017 - [c17]Anna Volokitin, Michael Gygli, Xavier Boix:
Predicting When Eye Fixations Are Consistent. AAAI Spring Symposia 2017 - 2016
- [j3]Ming Jiang
Learning to Predict Sequences of Human Visual Fixations. IEEE Trans. Neural Networks Learn. Syst. 27(6): 1241-1252 (2016) - [c16]Anna Volokitin, Michael Gygli, Xavier Boix:
Predicting When Saliency Maps are Accurate and Eye Fixations Consistent. CVPR 2016: 544-552 - [i5]Ece Ozkan, Gemma Roig
Herding Generalizes Diverse M -Best Solutions. CoRR abs/1611.04353 (2016) - 2015
- [b1]Xavier Boix:
Computational commonalities of image understanding algorithms. ETH Zurich, Zürich, Switzerland, 2015 - [j2]Michael Van den Bergh, Xavier Boix, Gemma Roig
SEEDS: Superpixels Extracted Via Energy-Driven Sampling. Int. J. Comput. Vis. 111(3): 298-314 (2015) - [c15]Ming Jiang, Xavier Boix, Juan Xu, Gemma Roig
Saliency Prediction with Active Semantic Segmentation. BMVC 2015: 15.1-15.13 - [c14]Xun Huang, Chengyao Shen, Xavier Boix, Qi Zhao:
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks. ICCV 2015: 262-270 - [i4]Yan Luo
Foveation-based Mechanisms Alleviate Adversarial Examples. CoRR abs/1511.06292 (2015) - 2014
- [c13]Alex Locher, Lennart Elsen, Xavier Boix, Luc Van Gool:
Reconstruction of Inextensible Surfaces on a Budget via Bootstrapping. 3DV 2014: 240-247 - [c12]Xavier Boix, Gemma Roig, Salomon Diether, Luc Van Gool:
Self-Adaptable Templates for Feature Coding. NIPS 2014: 864-872 - [i3]Xavier Boix, Gemma Roig
Comment on "Ensemble Projection for Semi-supervised Image Classification". CoRR abs/1408.6963 (2014) - 2013
- [c11]Xavier Boix, Michael Gygli, Gemma Roig
Sparse Quantization for Patch Description. CVPR 2013: 2842-2849 - [c10]Michael Van den Bergh, Gemma Roig
Online Video SEEDS for Temporal Window Objectness. ICCV 2013: 377-384 - [c9]Gemma Roig
Active MAP Inference in CRFs for Efficient Semantic Segmentation. ICCV 2013: 2312-2319 - [i2]Gemma Roig
Random Binary Mappings for Kernel Learning and Efficient SVM. CoRR abs/1307.5161 (2013) - [i1]Michael Van den Bergh, Xavier Boix, Gemma Roig, Luc Van Gool:
SEEDS: Superpixels Extracted via Energy-Driven Sampling. CoRR abs/1309.3848 (2013) - 2012
- [j1]Xavier Boix, Josep M. Gonfaus, Joost van de Weijer
Harmony Potentials - Fusing Global and Local Scale for Semantic Image Segmentation. Int. J. Comput. Vis. 96(1): 83-102 (2012) - [c8]Michael Van den Bergh, Xavier Boix, Gemma Roig, Benjamin de Capitani, Luc Van Gool:
SEEDS: Superpixels Extracted via Energy-Driven Sampling. ECCV (7) 2012: 13-26 - [c7]Xavier Boix, Gemma Roig
Nested Sparse Quantization for Efficient Feature Coding. ECCV (2) 2012: 744-758 - [c6]Roderick de Nijs, Sebastian Ramos, Gemma Roig
On-line semantic perception using uncertainty. IROS 2012: 4185-4191 - 2011
- [c5]Gemma Roig
Hierarchical CRF with product label spaces for parts-based models. FG 2011: 657-664 - [c4]Aurélien Lucchi
Are spatial and global constraints really necessary for segmentation? ICCV 2011: 9-16 - [c3]Gemma Roig
Conditional Random Fields for multi-camera object detection. ICCV 2011: 563-570 - 2010
- [c2]Josep M. Gonfaus, Xavier Boix Bosch, Joost van de Weijer
Harmony potentials for joint classification and segmentation. CVPR 2010: 3280-3287
2000 – 2009
- 2009
- [c1]Gemma Roig, Xavier Boix, Fernando De la Torre:
Optimal feature selection for subspace image matching. ICCV Workshops 2009: 200-205
Coauthor Index
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last updated on 2024-10-07 21:16 CEST by the dblp team
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