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 ArticleDOI
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
TL;DR: Li et al. as mentioned in this paper proposed to search the network level structure in addition to the cell level structure, which formed a hierarchical architecture search space and achieved state-of-the-art performance without any ImageNet pretraining.
Proceedings ArticleDOI
What, where and who? Classifying events by scene and object recognition
Li-Jia Li,Li Fei-Fei +1 more
TL;DR: This paper uses a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification and proposes a first attempt to classify events in static images by integrating scene and object categorizations.
Book ChapterDOI
What’s the Point: Semantic Segmentation with Point Supervision
TL;DR: This work takes a natural step from image-level annotation towards stronger supervision: it asks annotators to point to an object if one exists, and incorporates this point supervision along with a novel objectness potential in the training loss function of a CNN model.
Book ChapterDOI
Modeling temporal structure of decomposable motion segments for activity classification
TL;DR: A framework for modeling motion by exploiting the temporal structure of the human activities, which represents activities as temporal compositions of motion segments, and shows that the algorithm performs better than other state of the art methods.
Proceedings ArticleDOI
Learning object categories from Google's image search
TL;DR: A new model, TSI-pLSA, is developed, which extends pLSA (as applied to visual words) to include spatial information in a translation and scale invariant manner, and can handle the high intra-class variability and large proportion of unrelated images returned by search engines.