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Computer Vision
1663 directly classified papers
Papers per year
2006: 8
2007: 6
2008: 4
2009: 4
2010: 1
2011: 7
2012: 9
2013: 45
2014: 23
2015: 21
2016: 20
2017: 33
2018: 39
2019: 72
2020: 147
2021: 145
2022: 165
2023: 214
2024: 336
2025: 346
2026: 18
Papers
Searching for objects driven by context
NIPS 2012
Localizing 3D cuboids in single-view images
NIPS 2012
On the connections between saliency and tracking
NIPS 2012
Shifting Weights: Adapting Object Detectors from Image to Video
NIPS 2012
Learning about Canonical Views from Internet Image Collections
NIPS 2012
Recognizing Activities by Attribute Dynamics
NIPS 2012
Controlled Recognition Bounds for Visual Learning and Exploration
NIPS 2012
A lattice filter model of the visual pathway
NIPS 2012
Object Detection with Grammar Models
NIPS 2011
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities
NIPS 2011
Transfer Learning by Borrowing Examples for Multiclass Object Detection
NIPS 2011
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound
NIPS 2011
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
NIPS 2011
Image Parsing with Stochastic Scene Grammar
NIPS 2011
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines
NIPS 2011
A Bayesian Framework for Figure-Ground Interpretation
NIPS 2010
Structural inference affects depth perception in the context of potential occlusion
NIPS 2009
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
NIPS 2009
Individuation, Identification and Object Discovery
NIPS 2009
Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out
NIPS 2009
Optimization on a Budget: A Reinforcement Learning Approach
NIPS 2008
Learning a discriminative hidden part model for human action recognition
NIPS 2008
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
NIPS 2008
Recursive Segmentation and Recognition Templates for 2D Parsing
NIPS 2008
Congruence between model and human attention reveals unique signatures of critical visual events
NIPS 2007
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