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51
Practical bayesian optimization of machine learning algorithms
, 2012
"... In this section we specify additional details of our Bayesian optimization algorithm which, for brevity, were omitted from the paper. For more detail, the code used in this work is made publicly available at ..."
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Cited by 130 (16 self)
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In this section we specify additional details of our Bayesian optimization algorithm which, for brevity, were omitted from the paper. For more detail, the code used in this work is made publicly available at
How important are ‘deformable parts’ in the deformable parts model
- In ECCV Workshop on Parts and Attributes
, 2012
"... Abstract. The Deformable Parts Model (DPM) has recently emerged as a very useful and popular tool for tackling the intra-category diversity problem in object detection. In this paper, we summarize the key insights from our empirical analysis of the important elements constituting this detector. More ..."
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Cited by 41 (4 self)
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Abstract. The Deformable Parts Model (DPM) has recently emerged as a very useful and popular tool for tackling the intra-category diversity problem in object detection. In this paper, we summarize the key insights from our empirical analysis of the important elements constituting this detector. More specifically, we study the relationship between the role of deformable parts and the mixture model components within this detector, and understand their relative importance. First, we find that by increasing the number of components, and switching the initialization step from their aspect-ratio, left-right flipping heuristics to appearancebased clustering, considerable improvement in performance is obtained. But more intriguingly, we observed that with these new components, the part deformations can now be turned off, yet obtaining results that are almost on par with the original DPM detector.
Learning the easy things first: Self-paced visual category discovery
- In CVPR
"... Objects vary in their visual complexity, yet existing discovery methods perform “batch ” clustering, paying equal attention to all instances simultaneously—regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances f ..."
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Cited by 27 (0 self)
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Objects vary in their visual complexity, yet existing discovery methods perform “batch ” clustering, paying equal attention to all instances simultaneously—regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances first, and progressively expands its repertoire to include more complex objects. Easier regions are defined as those with both high likelihood of generic objectness and high familiarity of surrounding objects. At each cycle of the discovery process, we re-estimate the easiness of each subwindow in the pool of unlabeled images, and then retrieve a single prominent cluster from among the easiest instances. Critically, as the system gradually accumulates models, each new (more difficult) discovery benefits from the context provided by earlier discoveries. Our experiments demonstrate the clear advantages of self-paced discovery relative to conventional batch approaches, including both more accurate summarization as well as stronger predictive models for novel data. 1.
How Do Humans Teach: On Curriculum Learning and Teaching Dimension
"... We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot. 1 Previous studies of computational teaching, particularly the teaching dimension model and the curriculum learning principle, offer contradictory predictions on what optimal st ..."
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Cited by 21 (6 self)
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We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot. 1 Previous studies of computational teaching, particularly the teaching dimension model and the curriculum learning principle, offer contradictory predictions on what optimal strategy the teacher should follow in this teaching task. We show through behavioral studies that humans employ three distinct teaching strategies, one of which is consistent with the curriculum learning principle, and propose a novel theoretical framework as a potential explanation for this strategy. This framework, which assumes a teaching goal of minimizing the learner’s expected generalization error at each iteration, extends the standard teaching dimension model and offers a theoretical justification for curriculum learning. 1
Learning Collections of Part Models for Object Recognition
"... We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part ..."
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Cited by 20 (1 self)
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We propose a method to learn a diverse collection of discriminative parts from object bounding box annotations. Part detectors can be trained and applied individually, which simplifies learning and extension to new features or categories. We apply the parts to object category detection, pooling part detections within bottom-up proposed regions and using a boosted classifier with proposed sigmoid weak learners for scoring. On PASCAL VOC 2010, we evaluate the part detectors ’ ability to discriminate and localize annotated keypoints. Our detection system is competitive with the best-existing systems, outperforming other HOG-based detectors on the more deformable categories. 1.
Descriptor Learning Using Convex Optimisation
- in European Conference on Computer Vision
, 2012
"... Abstract. The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a conve ..."
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Cited by 19 (0 self)
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Abstract. The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality. Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. And, finally, we employ a state-of-the-art stochastic optimizer that is efficient and well matched to the non-smooth cost functions proposed here. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. [2], and large scale object retrieval, Philbin et al. [10]. 1
Max-Margin Min-Entropy Models
- AISTATS
, 2012
"... We propose a novel family of discriminative lvms, called max-margin min-entropy (m3e) models, that predicts the output by minimizing the Rényi entropy [18] of the corresponding generalized distribuhal-00773602, ..."
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Cited by 16 (4 self)
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We propose a novel family of discriminative lvms, called max-margin min-entropy (m3e) models, that predicts the output by minimizing the Rényi entropy [18] of the corresponding generalized distribuhal-00773602,
Shifting Weights: Adapting Object Detectors from Image to Video
"... Typical object detectors trained on images perform poorly on video, as there is a clear distinction in domain between the two types of data. In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos. We treat the problem as one of unsupervised domai ..."
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Cited by 12 (2 self)
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Typical object detectors trained on images perform poorly on video, as there is a clear distinction in domain between the two types of data. In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos. We treat the problem as one of unsupervised domain adaptation, in which we are given labeled data from the source domain (image), but only unlabeled data from the target domain (video). Our approach, self-paced domain adaptation, seeks to iteratively adapt the detector by re-training the detector with automatically discovered target domain examples, starting with the easiest first. At each iteration, the algorithm adapts by considering an increased number of target domain examples, and a decreased number of source domain examples. To discover target domain examples from the vast amount of video data, we introduce a simple, robust approach that scores trajectory tracks instead of bounding boxes. We also show how rich and expressive features specific to the target domain can be incorporated under the same framework. We show promising results on the 2011 TRECVID Multimedia Event Detection [1] and LabelMe Video [2] datasets that illustrate the benefit of our approach to adapt object detectors to video. 1
G.: Latent maximum margin clustering
, 2013
"... We present a maximum margin framework that clusters data using latent vari-ables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learn-ing, and develop an alternating descent algorithm to effectively solv ..."
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Cited by 9 (4 self)
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We present a maximum margin framework that clusters data using latent vari-ables. Using latent representations enables our framework to model unobserved information embedded in the data. We implement our idea by large margin learn-ing, and develop an alternating descent algorithm to effectively solve the resultant non-convex optimization problem. We instantiate our latent maximum margin clustering framework with tag-based video clustering tasks, where each video is represented by a latent tag model describing the presence or absence of video tags. Experimental results obtained on three standard datasets show that the proposed method outperforms non-latent maximum margin clustering as well as conven-tional clustering approaches. 1
Kernel Latent SVM for Visual Recognition
"... Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision. However, a limitation of LSVMs is that they rely on linear models. For many computer vision tasks, linear models are suboptimal and nonlinear models learned with kernels typ ..."
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Cited by 8 (4 self)
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Latent SVMs (LSVMs) are a class of powerful tools that have been successfully applied to many applications in computer vision. However, a limitation of LSVMs is that they rely on linear models. For many computer vision tasks, linear models are suboptimal and nonlinear models learned with kernels typically perform much better. Therefore it is desirable to develop the kernel version of LSVM. In this paper, we propose kernel latent SVM (KLSVM) – a new learning framework that combines latent SVMs and kernel methods. We develop an iterative training algorithm to learn the model parameters. We demonstrate the effectiveness of KLSVM using three different applications in visual recognition. Our KLSVM formulation is very general and can be applied to solve a wide range of applications in computer vision and machine learning. 1
