-Jia Li
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Featured researches published by -Jia Li.
computer vision and pattern recognition | 2009
Jia Deng; Wei Dong; Richard Socher; Li-Jia Li; Kai Li; Li Fei-Fei
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
international conference on computer vision | 2007
Li-Jia Li; Li Fei-Fei
We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.
International Journal of Computer Vision | 2017
Ranjay Krishna; Yuke Zhu; Oliver Groth; Justin Johnson; Kenji Hata; Joshua Kravitz; Stephanie Chen; Yannis Kalantidis; Li-Jia Li; David A. Shamma; Michael S. Bernstein; Li Fei-Fei
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of
computer vision and pattern recognition | 2007
Li-Jia Li; Gang Wang; Li Fei-Fei
Communications of The ACM | 2016
Bart Thomee; David A. Shamma; Gerald Friedland; Benjamin Elizalde; Karl Ni; Douglas N. Poland; Damian Borth; Li-Jia Li
35
international conference on multimedia retrieval | 2015
Sachin Sudhakar Farfade; Mohammad J. Saberian; Li-Jia Li
International Journal of Computer Vision | 2010
Li-Jia Li; Li Fei-Fei
35 objects,
computer vision and pattern recognition | 2015
Justin Johnson; Ranjay Krishna; Michael Stark; Li-Jia Li; David A. Shamma; Michael S. Bernstein; Li Fei-Fei
computer vision and pattern recognition | 2010
Li-Jia Li; Chong Wang; Yongwhan Lim; David M. Blei; Li Fei-Fei
26
computer vision and pattern recognition | 2014
Kevin Tang; Armand Joulin; Li-Jia Li; Li Fei-Fei