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94
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
"... We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks an ..."
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Cited by 188 (22 self)
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We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be repurposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and finegrained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the stateoftheart on several important vision challenges. We are releasing DeCAF, an opensource implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms. 1.
Classifier Chains for Multilabel Classification
"... Abstract. The widely known binary relevance method for multilabel classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its labelindependence assumption. Instead, most current methods invest considerable ..."
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Cited by 153 (12 self)
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Abstract. The widely known binary relevance method for multilabel classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its labelindependence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevancebased methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multilabel datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and stateoftheart methods, both in terms of predictive performance and time complexity. 1
Label Embedding Trees for Large MultiClass Tasks
"... Multiclass classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm f ..."
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Cited by 85 (2 self)
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Multiclass classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a treestructure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than nonembedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including OnevsRest while being orders of magnitude faster. 1
Large Scale MaxMargin MultiLabel Classification with Priors
"... We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this ..."
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Cited by 40 (2 self)
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We propose a maxmargin formulation for the multilabel classification problem where the goal is to tag a data point with a set of prespecified labels. Given a set of L labels, a data point can be tagged with any of the 2 L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Existing solutions take either of two approaches. The first assumes, a priori, that there are no label correlations and independently trains a classifier for each label (as is done in the 1vsAll heuristic). This reduces the problem complexity from exponential to linear and such methods can scale to large problems. The second approach explicitly models correlations by pairwise label interactions. However, the complexity remains exponential unless one assumes that label correlations are sparse. Furthermore, the learnt correlations reflect the training set biases. We take a middle approach that assumes labels are correlated but does not incorporate pairwise label terms in the prediction function. We show that the complexity can still be reduced from exponential to linear while modelling dense pairwise label correlations. By incorporating correlation priors we can overcome training set biases and improve prediction accuracy. We provide a principled interpretation of the 1vsAll method and show
Multiinstance multilabel learning
 Artificial Intelligence
"... In this paper, we propose the MIML (MultiInstance MultiLabel learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicate ..."
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Cited by 38 (16 self)
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In this paper, we propose the MIML (MultiInstance MultiLabel learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Consideringthat the degeneration process may lose information, we propose the DMimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming singleinstances into the MIML representation for learning, while SubCod works by transforming singlelabel examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the singleinstances or singlelabel examples directly.
Kernel Belief Propagation
"... We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the ..."
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Cited by 31 (12 self)
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We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results. 1
Error Correcting Tournaments
, 2008
"... Abstract. We present a family of adaptive pairwise tournaments that are provably robust against large error fractions when used to determine the largest element in a set. The tournaments use nk pairwise comparisons but have only O(k + log n) depth, where n is the number of players and k is the robus ..."
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Cited by 26 (4 self)
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Abstract. We present a family of adaptive pairwise tournaments that are provably robust against large error fractions when used to determine the largest element in a set. The tournaments use nk pairwise comparisons but have only O(k + log n) depth, where n is the number of players and k is the robustness parameter (for reasonable values of n and k). These tournaments also give a reduction from multiclass to binary classification in machine learning, yielding the best known analysis for the problem. 1
Multilabel classification on tree and DAGstructured hierarchies
 In ICML
, 2011
"... Many realworld applications involve multilabel classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in treestructured hierarchies. In t ..."
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Cited by 23 (2 self)
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Many realworld applications involve multilabel classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in treestructured hierarchies. In this paper, we present a novel hierarchical multilabel classification algorithm which can be used on both tree and DAGstructured hierarchies. The key idea is to formulate the search for the optimal consistent multilabel as the finding of the best subgraph in a tree/DAG. Using a simple greedy strategy, the proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies. Theoretical results guarantee the optimality of the obtained solution. Experiments are performed on a large number of functional genomics data sets. The proposed method consistently outperforms the stateoftheart method on both tree and DAGstructured hierarchies. 1.
Transduction with Matrix Completion: Three Birds with One Stone
"... We pose transductive classification as a matrix completion problem. By assuming the underlying matrix has a low rank, our formulation is able to handle three problems simultaneously: i) multilabel learning, where each item has more than one label, ii) transduction, where most of these labels are un ..."
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Cited by 20 (0 self)
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We pose transductive classification as a matrix completion problem. By assuming the underlying matrix has a low rank, our formulation is able to handle three problems simultaneously: i) multilabel learning, where each item has more than one label, ii) transduction, where most of these labels are unspecified, and iii) missing data, where a large number of features are missing. We obtained satisfactory results on several realworld tasks, suggesting that the low rank assumption may not be as restrictive as it seems. Our method allows for different loss functions to apply on the feature and label entries of the matrix. The resulting nuclear norm minimization problem is solved with a modified fixedpoint continuation method that is guaranteed to find the global optimum. 1
A MultiView Embedding Space for Modeling Internet Images, Tags, and their Semantics
 IJCV
"... This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as imagetoimage search, tagtoimage search, and imagetotag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping vis ..."
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Cited by 18 (1 self)
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This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as imagetoimage search, tagtoimage search, and imagetotag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporate a third view capturing highlevel image semantics, represented either by a single category or multiple nonmutuallyexclusive concepts. We present two ways to train the threeview embedding: supervised, with the third view coming from groundtruth labels or search keywords; and unsupervised, with semantic themes automatically obtained by clustering the tags. To ensure high accuracy for retrieval tasks while keeping the learning process scalable, we combine multiple strong visual features