Carl Vondrick

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Antonio Torralba63
Navneet Dalal40
Bill Triggs40
Andrew Zisserman39
Deva Ramanan39
36Antonio Torralba
20Martial Hebert
18Deva Ramanan
16Hamed Pirsiavash
16Juan Carlos Niebles

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2010
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HOGgles: Visualizing Object Detection Features
We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For… (More)
A large-scale benchmark dataset for event recognition in surveillance video
We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one… (More)
Efficiently Scaling up Crowdsourced Video Annotation
We present an extensive three year study on economically annotating video with crowdsourced marketplaces. Our public framework has annotated thousands of real world videos, including massive data sets unprecedented for their size, complexity, and cost. To accomplish this, we designed a state-of-the-art video annotation user interface and demonstrate that,… (More)
Do We Need More Training Data or Better Models for Object Detection?
Datasets for training object recognition systems are steadily growing in size. This paper investigates the question of whether existing detectors will continue to improve as data grows, or if models are close to saturating due to limited model complexity and the Bayes risk associated with the feature spaces in which they operate. We focus on the popular… (More)
Anticipating Visual Representations from Unlabeled Video
Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through… (More)
Anticipating the future by watching unlabeled video
In many computer vision applications, machines will need to reason beyond the present, and predict the future. This task is challenging because it requires leveraging extensive commonsense knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently obtaining this knowledge is through the massive amounts of… (More)
SoundNet: Learning Sound Representations from Unlabeled Video
We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two-million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet… (More)