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← Core AI
Computer Vision
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Core AI
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Computer Vision
1562 directly classified papers
Papers per year
2005: 1
2006: 5
2007: 7
2008: 6
2009: 7
2010: 5
2011: 2
2012: 14
2013: 57
2014: 14
2015: 44
2016: 25
2017: 40
2018: 69
2019: 78
2020: 151
2021: 136
2022: 183
2023: 171
2024: 237
2025: 299
2026: 11
Papers
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
NIPS 2016
Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification
ICML 2016
Discrete Deep Feature Extraction: A Theory and New Architectures
ICML 2016
Integrated perception with recurrent multi-task neural networks
NIPS 2016
Local Similarity-Aware Deep Feature Embedding
NIPS 2016
CliqueCNN: Deep Unsupervised Exemplar Learning
NIPS 2016
Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
ICML 2016
UAV Sensor Fusion With Latent-Dynamic Conditional Random Fields in Coronal Plane Estimation
CVPR 2016
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
JMLR 2016
Distribution-Matching Embedding for Visual Domain Adaptation
JMLR 2016
Dynamic Capacity Networks
ICML 2016
Group Equivariant Convolutional Networks
ICML 2016
Bottom-Up and Top-Down Reasoning With Hierarchical Rectified Gaussians
CVPR 2016
Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
CVPR 2016
An Efficient Exact-PGA Algorithm for Constant Curvature Manifolds
CVPR 2016
Sublabel-Accurate Relaxation of Nonconvex Energies
CVPR 2016
Learning to Match Aerial Images With Deep Attentive Architectures
CVPR 2016
Learning Deep Features for Discriminative Localization
CVPR 2016
Visualizing and Understanding Deep Texture Representations
CVPR 2016
Ambiguity Helps: Classification With Disagreements in Crowdsourced Annotations
CVPR 2016
Dense Human Body Correspondences Using Convolutional Networks
CVPR 2016
Shallow and Deep Convolutional Networks for Saliency Prediction
CVPR 2016
Detection and Accurate Localization of Circular Fiducials Under Highly Challenging Conditions
CVPR 2016
TI-Pooling: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
CVPR 2016
InterActive: Inter-Layer Activeness Propagation
CVPR 2016
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