Author
Jianke Zhu
Other affiliations: ETH Zurich, The Chinese University of Hong Kong, University of Electronic Science and Technology of China ...read more
Bio: Jianke Zhu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Image retrieval & Computer science. The author has an hindex of 36, co-authored 138 publications receiving 7389 citations. Previous affiliations of Jianke Zhu include ETH Zurich & The Chinese University of Hong Kong.
Papers published on a yearly basis
Papers
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06 Sep 2014TL;DR: This paper presents a very appealing tracker based on the correlation filter framework and suggests an effective scale adaptive scheme to tackle the problem of the fixed template size in kernel correlation filter tracker.
Abstract: Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.
1,298 citations
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03 Nov 2014
TL;DR: This paper investigates a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIr tasks under varied settings.
Abstract: Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known ``semantic gap'' issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolutional Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research.
865 citations
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University of Ljubljana1, University of Birmingham2, Czech Technical University in Prague3, Linköping University4, Austrian Institute of Technology5, Carnegie Mellon University6, Parthenope University of Naples7, University of Isfahan8, Autonomous University of Madrid9, University of Ottawa10, University of Oxford11, Hong Kong Baptist University12, Kyiv Polytechnic Institute13, Middle East Technical University14, Hacettepe University15, King Abdullah University of Science and Technology16, Pohang University of Science and Technology17, University of Nottingham18, University at Albany, SUNY19, Chinese Academy of Sciences20, Dalian University of Technology21, Xi'an Jiaotong University22, Indian Institute of Space Science and Technology23, Hong Kong University of Science and Technology24, ASELSAN25, Australian National University26, Commonwealth Scientific and Industrial Research Organisation27, University of Missouri28, University of Verona29, Universidade Federal de Itajubá30, United States Naval Research Laboratory31, Marquette University32, Graz University of Technology33, Naver Corporation34, Imperial College London35, Electronics and Telecommunications Research Institute36, Zhejiang University37, University of Surrey38, Harbin Institute of Technology39, Lehigh University40
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Abstract: The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).
744 citations
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University of Ljubljana1, University of Birmingham2, Czech Technical University in Prague3, Linköping University4, Austrian Institute of Technology5, Autonomous University of Madrid6, Parthenope University of Naples7, University of Isfahan8, University of Oxford9, Superior National School of Advanced Techniques10, Middle East Technical University11, Dalian University of Technology12, Chinese Academy of Sciences13, ASELSAN14, United States Naval Research Laboratory15, National University of Defense Technology16, University of Science and Technology of China17, Electronics and Telecommunications Research Institute18, Zhejiang University19, Beijing University of Posts and Telecommunications20, Huazhong University of Science and Technology21, University of Missouri22, Carnegie Mellon University23, General Electric24, King Abdullah University of Science and Technology25, University of California, Merced26, University of Surrey27, University at Albany, SUNY28
TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.
Abstract: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1.
485 citations
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25 Jun 2006TL;DR: A framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously and is more effective than the state-of-the-art algorithms for active learning.
Abstract: The goal of active learning is to select the most informative examples for manual labeling. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient since the classification model has to be retrained for every labeled example. In this paper, we present a framework for "batch mode active learning" that applies the Fisher information matrix to select a number of informative examples simultaneously. The key computational challenge is how to efficiently identify the subset of unlabeled examples that can result in the largest reduction in the Fisher information. To resolve this challenge, we propose an efficient greedy algorithm that is based on the property of submodular functions. Our empirical studies with five UCI datasets and one real-world medical image classification show that the proposed batch mode active learning algorithm is more effective than the state-of-the-art algorithms for active learning.
473 citations
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01 Jan 2009
TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
Abstract: The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data instances to be labeled by an oracle (e.g., a human annotator). Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant or easily obtained, but labels are difficult, time-consuming, or expensive to obtain. This report provides a general introduction to active learning and a survey of the literature. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. An analysis of the empirical and theoretical evidence for successful active learning, a summary of problem setting variants and practical issues, and a discussion of related topics in machine learning research are also presented.
5,227 citations
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TL;DR: In this article, a review of deep learning-based object detection frameworks is provided, focusing on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further.
Abstract: Due to object detection’s close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
3,097 citations