Index for moos

Moos, N.[Nicolai] * 2021: Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

Moosaei, M.[Maryam] * 2022: OutfitGAN: Learning Compatible Items for Generative Fashion Outfits

Moosavi Dezfooli, S.M.[Seyed Mohsen] * 2016: DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
* 2017: Robustness of Deep Networks: A Geometrical Perspective, The
* 2017: Universal Adversarial Perturbations
* 2018: Empirical Study of the Topology and Geometry of Deep Networks
* 2018: Geometric Robustness of Deep Networks: Analysis and Improvement
* 2019: Geometry-Inspired Decision-Based Attack, A
* 2019: Robustness via Curvature Regularization, and Vice Versa
* 2019: SparseFool: A Few Pixels Make a Big Difference
* 2020: GeoDA: A Geometric Framework for Black-Box Adversarial Attacks
* 2021: Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness
* 2022: PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
* 2022: Vehicle trajectory prediction works, but not everywhere
* 2023: Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training, The
* 2023: How to choose your best allies for a transferable attack?
Includes: Moosavi Dezfooli, S.M.[Seyed Mohsen] Moosavi-Dezfooli, S.M.[Seyed-Mohsen]
14 for Moosavi Dezfooli, S.M.

Moosavi, M.S.[Mahdiyeh Sadat] * 2023: Effect of Tactile Affordance During the Design of Extended Reality-based Training Environments for Healthcare Contexts

Moosavi, V. * 2013: Tree Crown Delineation on VHR Aerial Imagery with SVM Classification Technique Optimized by Taguchi Method: A Case Study in Zagros Woodlands
* 2019: Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping, An
Includes: Moosavi, V. Moosavi, V.[Vahid]

Moosbauer, S. * 2020: Evaluation of Objective Image Quality Assessment for Thermal Infrared Video Tone Mapping, An
* 2021: Deep Fusion of Appearance and Frame Differencing for Motion Segmentation
Includes: Moosbauer, S. Moosbauer, S.[Sebastian]

Moose, S.[Stephen] * 2022: Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data

Mooser, J.[Jonathan] * 2009: Applying robust structure from motion to markerless augmented reality
* 2009: Dynamic Programming Approach to Maximizing Tracks for Structure from Motion, A

Mooser, R.[Rene] * 2013: Estimation of affine transformations directly from tomographic projections in two and three dimensions
Includes: Mooser, R.[Rene] Mooser, R.[René]

Mooshofer, H. * 1999: video segmentation algorithm for hierarchical object representations and its implementation, A

Moosmann, F.[Frank] * 2008: Randomized Clustering Forests for Image Classification
* 2011: Unsupervised discovery of object classes in 3D outdoor scenarios
* 2012: Team AnnieWAY's Entry to the 2011 Grand Cooperative Driving Challenge
* 2021: SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
Includes: Moosmann, F.[Frank] Moosmann, F.

Moosmayr, T.[Tobias] * 2008: Switching Linear Dynamic Models for Noise Robust In-Car Speech Recognition

Index for "m"


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