L
Li Fei-Fei
Researcher at Stanford University
Publications - 515
Citations - 199224
Li Fei-Fei is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 120, co-authored 420 publications receiving 145574 citations. Previous affiliations of Li Fei-Fei include Google & California Institute of Technology.
Papers
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Proceedings Article
Crowdsourcing annotations for visual object detection
Hao Su,Jia Deng,Li Fei-Fei +2 more
TL;DR: The key observation is that drawing a bounding box is significantly more difficult and time consuming than giving answers to multiple choice questions, so quality control through additional verification tasks is more cost effective than consensus based algorithms.
Proceedings ArticleDOI
ReVision: automated classification, analysis and redesign of chart images
TL;DR: ReVision is a system that automatically redesigns visualizations to improve graphical perception, and applies perceptually-based design principles to populate an interactive gallery of redesigned charts.
Book ChapterDOI
Reasoning about Object Affordances in a Knowledge Base Representation
TL;DR: This work learns a knowledge base (KB) using a Markov Logic Network (MLN) and shows that a diverse set of visual inference tasks can be done in this unified framework without training separate classifiers, including zero-shot affordance prediction and object recognition given human poses.
Proceedings ArticleDOI
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
Li-Jia Li,Gang Wang,Li Fei-Fei +2 more
TL;DR: This work adapts a non-parametric graphical model and proposes an incremental learning framework that mimics the human learning process of iteratively accumulating model knowledge and image examples and is capable of collecting image datasets that are superior to Caltech 101 and LabelMe.
Journal ArticleDOI
Recognizing Human-Object Interactions in Still Images by Modeling the Mutual Context of Objects and Human Poses
Bangpeng Yao,Li Fei-Fei +1 more
TL;DR: This paper proposes a mutual context model to jointly model objects and human poses in human-object interaction activities and shows that the model outperforms state of the art in detecting very difficult objects and estimating human poses, as well as classifying human- object interaction activities.