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Claim this profileWorks222 from public data
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- A Comprehensive Overhaul of Feature Distillation788
A novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function, which achieves a significant performance improvement in all tasks.
- The Visual Object Tracking VOT2016 Challenge Results776
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.
- Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons637
This paper proposes a knowledge transfer method via distillation of activation boundaries formed by hidden neurons and proposes an activation transfer loss that has the minimum value when the boundaries generated by the student coincide with those by the teacher.
- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
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- Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning291
A new numerically stable form of the Laplacian sharpening by incorporating the signed graphs is proposed and a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks.
- The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification238
A novel minority over-sampling method to augment di-versified minority samples by leveraging the rich context of the majority classes as background images, which achieves state-of-the-art performance on various long-tailed classification benchmarks.
- Context-Aware Deep Feature Compression for High-Speed Visual Tracking229
A new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers and introduces extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert autoencoders.
- Influence-Balanced Loss for Imbalanced Visual Classification198
A new loss is derived used in the balancing training phase that alleviates the influence of samples that cause an overfitted decision boundary and outperforms the state-of-the-art cost-sensitive loss methods.
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- Intelligent visual surveillance — A survey181
The first part surveys image enhancement, moving object detection and tracking, and motion behavior understanding, and the second part reviews wide-area surveillance techniques based on the fusion of multiple visual sensors, camera calibration and cooperative camera systems.
- Sensitivity analysis of multilayer perceptron with differentiable activation functions180
A sensitivity depending on the weight set in a single-output multilayer perceptron (MLP) with differentiable activation functions is proposed and computer simulations have been performed, resulting in good agreement between theoretical and simulation outcomes for small weight perturbations.
- Knowledge Distillation with Adversarial Samples Supporting Decision Boundary163
A new perspective based on a decision boundary is provided, which is one of the most important component of a classifier, and the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary.
- Flux observer with online tuning of stator and rotor resistances for induction motors124
Simulations and actual experiments show that the rotor flux is estimated in all operating conditions and that both resistances converge to their actual values when the electrical motor torque exists.
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- VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results87
The evaluation protocol of the VisDrone-SOT2018 challenge is presented and the results of a comparison of 22 trackers on the benchmark dataset are presented, which are publicly available on the challenge website.
- Detection of moving objects with a moving camera using non-panoramic background model86
A small-size background model is built by the proposed single spatio-temporal distributed Gaussian model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance.
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- Nonlinear Adaptive Control Using Networks of76
A stable nonparametric adaptive control approach using a piecewise local linear approximator with universal approximation capability and analyses of asymptotic stability of the tracking error and parameter convergence for the proposed adaptive control scheme with the on-line self-organizing structure are presented.
- Nonlinear adaptive control using networks of piecewise linear approximators76
A stable nonparametric adaptive control approach using a piecewise local linear approximator with universal approximation capability and analyses of asymptotic stability of the tracking error and parameter convergence for the proposed adaptive control scheme with the on-line self-organizing structure are presented.
- PMnet: learning of disentangled pose and movement for unsupervised motion retargeting72
The proposed deep learning framework for unsupervised motion retargeting outperforms the state-of-the-art (sota) method by reducing the motion retargeting error from 7.68 (sota) to 1.95 (the authors').
- Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image65
A new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor, which can detect cancerous tissues efficiently and high sensitivity by the measurement of the number of correctly classified pixels.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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