Veit, A.
* 2015: Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences
* 2016: Learning to Detect and Match Keypoints with Deep Architectures
* 2017: Conditional Similarity Networks
* 2017: Learning from Noisy Large-Scale Datasets with Minimal Supervision
* 2018: Convolutional Networks with Adaptive Inference Graphs
* 2018: Learning to Evaluate Image Captioning
* 2018: Separating Self-Expression and Visual Content in Hashtag Supervision
* 2020: Convolutional Networks with Adaptive Inference Graphs
* 2021: Understanding Robustness of Transformers for Image Classification
Includes: Veit, A. Veit, A.[Andreas]
9 for Veit, A.
Veit, M.[Manuel]
* 2014: Go'Then'Tag: A 3-D point cloud annotation technique
Veit, T.[Thomas]
* 2004: a contrario decision framework for motion detection, An
* 2004: Probabilistic parameter-free motion detection
* 2005: Maximality Principle Applied to a Contrario Motion Detection, A
* 2006: a contrario Decision Framework for Region-Based Motion Detection, An
* 2019: Analysis and Implementation of the FFDNet Image Denoising Method, An
* 2019: DVDNET: A Fast Network for Deep Video Denoising
* 2020: FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
* 2021: Analysis and Implementation of the HDR+ Burst Denoising Method, An
Includes: Veit, T.[Thomas] Veit, T.
8 for Veit, T.
Veitch Michaelis, J.
* 2015: Data Fusion of Lidar into a Region Growing Stereo Algorithm
* 2016: Crack Detection in As-Cast Steel Using Laser Triangulation and Machine Learning
* 2016: Enhancement Of Stereo Imagery By Artificial Texture Projection Generated Using A Lidar
* 2016: Optimised System For Generating Multi-resolution DTMS Using NASA MRO Datasets, An
Includes: Veitch Michaelis, J. Veitch-Michaelis, J. Veitch-Michaelis, J.[Joshua]
Veith, T.L.[Tamie L.]
* 2018: Evaluating Concentrated Flowpaths in Riparian Forest Buffer Contributing Areas Using LiDAR Imagery and Topographic Metrics