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Results 1 - 10
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439
Learning to detect unseen object classes by betweenclass attribute transfer
- In CVPR
, 2009
"... We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of t ..."
Abstract
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Cited by 363 (5 self)
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We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens
Visual Odometry
- Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR’04
, 2004
"... We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched bet ..."
Abstract
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Cited by 299 (5 self)
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platforms. We focus on results with an autonomous ground vehicle. We give examples of camera trajectories estimated purely from images over previously unseen distances and periods of time. 1.
Inferring Unseen Views of People
, 2014
"... We pose unseen view synthesis as a probabilistic tensor completion problem. Given images of people organized by their rough viewpoint, we form a 3D appearance tensor indexed by images (pose examples), viewpoints, and image positions. After discovering the low-dimensional latent factors that approxim ..."
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We pose unseen view synthesis as a probabilistic tensor completion problem. Given images of people organized by their rough viewpoint, we form a 3D appearance tensor indexed by images (pose examples), viewpoints, and image positions. After discovering the low-dimensional latent factors
ProgressiveRandomization:SeeingtheUnseen
"... In this paper, we introduce the Progressive Randomization (PR): a new image meta-description approach suitable for different image inference applications such as broad class Image Categorization and Steganalysis. The main difference among PR and the state-of-the-art algorithms is that it is based on ..."
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on progressive perturbations on pixel values of images. With such perturbations, PR captures the image class separability allowing us to successfully infer high-level information about images. Even when only a limited number of training examples are available, the method still achieves good separability, and its
PERSPECTIVES PSYCHOLOGY The Unseen Mind
"... The human mind operates largely out of view, and yet people are unaware of their unawareness, confabulating reasons for their actions and preferences. Can people think they are undecided about a political issue after they have already made up their minds? The study by Galdi et al., on page 1100 in t ..."
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negative meaning. A common finding, using such implicit measures, is that people’s automatic responses correspond poorly to their self-reported attitudes (8–10). For example, Galdi et al. found that the correlation between people’s automatic
Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks
- In Proceedings of the Eighth National Conference on Artificial Intelligence
, 1990
"... Standard algorithms for explanation-based learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to refine approximately correct knowledge. This knowledge is used to determine the structure of an artificial neural ..."
Abstract
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Cited by 195 (15 self)
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neural network and the weights on its links, thereby making the knowledge accessible for modification by neural learning. KBANN is evaluated by empirical tests in the domain of molecular biology. Networks created by KBANN are shown to be superior, in terms of their ability to correctly classify unseen
Consistent Estimation of the Number of Unseen Elements
"... We observe a sample of text of n tokens from a large corpus of written text and note the occurrence of N distinct word types. We then ask what the total number of unseen word types in the population from which the sample was drawn is. The commonly used LNRE (large number of rare events) regime sugge ..."
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the number of unseen words in a corpus ((Efron and Thisted, 1976) studies the number of words Shakespeare knew but did not use) serve as motivational examples to the scenario of our focus.
Learning to Recognize Objects from Unseen Modalities
"... Abstract. In this paper we investigate the problem of exploiting multiple sources of information for object recognition tasks when additional modalities that are not present in the labeled training set are available for inference. This scenario is common to many robotics sensing applications and is ..."
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Cited by 7 (0 self)
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and is in contrast with the assumption made by existing approaches that require at least some labeled examples for each modality. To leverage the previously unseen features, we make use of the unlabeled data to learn a mapping from the existing modalities to the new ones. This allows us to predict the missing data
Exploiting language models to recognize unseen actions
- In ICMR
, 2013
"... This paper addresses the problem of human action recogni-tion. Typically, visual action recognition systems need visual training examples for all actions that one wants to recognize. However, the total number of possible actions is staggering as not only are there many types of actions but also many ..."
Abstract
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Cited by 3 (1 self)
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many possible objects for each action type. Normally, visual train-ing examples are needed for all actions of this combinatorial explosion of possibilities. To address this problem, this paper is a first attempt to propose a general framework for unseen action recognition in still images by exploiting
Quantifying Constructional Productivity with Unseen Slot Members
"... This paper is concerned with the possibility of quantifying and comparing the productivity of similar yet distinct syntactic constructions, predicting the likelihood of encountering unseen lexemes in their unfilled slots. Two examples are explored: variants of comparative correlative constructions ( ..."
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This paper is concerned with the possibility of quantifying and comparing the productivity of similar yet distinct syntactic constructions, predicting the likelihood of encountering unseen lexemes in their unfilled slots. Two examples are explored: variants of comparative correlative constructions
Results 1 - 10
of
439
