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 Jitendra M Malik

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Average citations per article90.64
Citation Count11,511
Publication count127
Publication years1986-2017
Available for download10
Average downloads per article1,701.10
Downloads (cumulative)17,011
Downloads (12 Months)690
Downloads (6 Weeks)95
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127 results found Export Results: bibtexendnoteacmrefcsv

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1
April 2017 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 39 Issue 4, April 2017
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as a feature representation. However, the information in this layer may be too coarse spatially to allow precise localization. On the contrary, earlier layers may be precise in localization but will not capture semantics. To ...

2
April 2017 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 39 Issue 4, April 2017
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional ...

3
March 2017 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 39 Issue 3, March 2017
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

Light-field cameras are quickly becoming commodity items, with consumer and industrial applications. They capture many nearby views simultaneously using a single image with a micro-lens array, thereby providing a wealth of cues for depth recovery: defocus, correspondence, and shading. In particular, apart from conventional image shading, one can refocus images ...

4
January 2017 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 39 Issue 1, January 2017
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 1

We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a ...

5
December 2016 NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 6,   Downloads (Overall): 6

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We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects. ...

6
June 2016 ICML'16: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48
Publisher: JMLR.org
Bibliometrics:
Citation Count: 0

Existing methods for retrieving k -nearest neighbours suffer from the curse of dimensionality. We argue this is caused in part by inherent deficiencies of space partitioning, which is the underlying strategy used by most existing methods. We devise a new strategy that avoids partitioning the vector space and present a ...

7
March 2016 Pattern Recognition Letters: Volume 72 Issue C, March 2016
Publisher: Elsevier Science Inc.
Bibliometrics:
Citation Count: 1

Bidirectional interactions between recognition, reconstruction and re-organization are very important.Bottom-up grouping generates object candidates, which can be classified top down, following which the segmentations are refined again.Recognition of 3D objects benefits from a reconstruction of 3D structure.3D reconstruction benefits from object category-specific priors.Reconstruction of 3D structure from video data goes ...
Keywords: 3D models, Object recognition, Action recognition: grouping, Shape reconstruction, Segmentation

8
January 2016 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 38 Issue 1, January 2016
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 37

Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves ...

9
April 2015 International Journal of Computer Vision: Volume 112 Issue 2, April 2015
Publisher: Kluwer Academic Publishers
Bibliometrics:
Citation Count: 13

In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (Silberman et al., ECCV, 2012). We propose algorithms ...
Keywords: RGB-D image segmentation, RGB-D scene classification, RGB-D semantic segmentation, RGB-D contour detection, RGB-D object detection

10 published by ACM
March 2015 AI Matters: Volume 1 Issue 3, March 2015
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 1,   Downloads (12 Months): 4,   Downloads (Overall): 48

Full text available: PDFPDF
Detectors trained on natural images can detect parts that characterize person figures in Cubist paintings.

11
December 2014 NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1
Publisher: MIT Press
Bibliometrics:
Citation Count: 1

Extracting 3D shape of deforming objects in monocular videos, a task known as non-rigid structure-from-motion (NRSfM), has so far been studied only on synthetic datasets and controlled environments. Typically, the objects to reconstruct are pre-segmented, they exhibit limited rotations and occlusions, or full-length trajectories are assumed. In order to integrate ...

12
January 2013 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 35 Issue 1, January 2013
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 20

We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like intersection and chi-squared kernels. ...
Keywords: Image classification, support vector machines, efficient classifiers, additive kernels

13
October 2012 ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 15

We present SIRFS (shape, illumination, and reflectance from shading), the first unified model for recovering shape, chromatic illumination, and reflectance from a single image. Our model is an extension of our previous work [1], which addressed the achromatic version of this problem. Dealing with color requires a modified problem formulation, ...

14
October 2012 ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 24

Object detection has over the past few years converged on using linear SVMs over HOG features. Training linear SVMs however is quite expensive, and can become intractable as the number of categories increase. In this work we revisit a much older technique, viz. Linear Discriminant Analysis, and show that LDA ...

15
October 2012 ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 5

In this paper, we propose a multi-component approach for object detection. Rather than attempting to represent an object category with a monolithic model, or pre-defining a reduced set of aspects, we form visual clusters from the data that are tight in appearance and configuration spaces. We train individual classifiers for ...

16
November 2011 ICCV '11: Proceedings of the 2011 International Conference on Computer Vision
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 30

We propose a method for recognizing attributes, such as the gender, hair style and types of clothes of people under large variation in viewpoint, pose, articulation and occlusion typical of personal photo album images. Robust attribute classifiers under such conditions must be invariant to pose, but inferring the pose in ...

17
May 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 33 Issue 5, May 2011
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 329

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of ...
Keywords: computer vision., image segmentation, Contour detection, Contour detection, image segmentation, computer vision.

18
March 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence: Volume 33 Issue 3, March 2011
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 113

Optical flow estimation is classically marked by the requirement of dense sampling in time. While coarse-to-fine warping schemes have somehow relaxed this constraint, there is an inherent dependency between the scale of structures and the velocity that can be estimated. This particularly renders the estimation of detailed human motion problematic, ...
Keywords: Motion, Motion, video, optical flow., video, optical flow.

19
September 2010 ECCV'10: Proceedings of the 11th European conference on Computer vision: Part V
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 74

Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. While pure bottom-up segmentation from static cues is well known to be ambiguous at the object level, the story changes as soon ...

20
September 2010 ECCV'10: Proceedings of the 11th European conference on Computer vision: Part VI
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 54

Bourdev and Malik (ICCV 09) introduced a new notion of parts, poselets, constructed to be tightly clustered both in the configuration space of keypoints, as well as in the appearance space of image patches. In this paper we develop a new algorithm for detecting people using poselets. Unlike that work ...



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