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Works138 from public data
- ECO: Efficient Convolution Operators for Tracking2,708
This work revisit the core DCF formulation and introduces a factorized convolution operator, which drastically reduces the number of parameters in the model, and a compact generative model of the training sample distribution that significantly reduces memory and time complexity, while providing better diversity of samples.
- RePaint: Inpainting using Denoising Diffusion Probabilistic Models2,341
This work proposes RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks and outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.
- Accurate Scale Estimation for Robust Visual Tracking2,087
This paper presents a novel approach to robust scale estimation that can handle large scale variations in complex image sequences and shows promising results in terms of accuracy and efficiency.
- Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking1,770
Discriminative Correlation Filters have demonstrated excellent performance for visual object tracking and the key to their success is the ability to efficiently exploit available negative data.
- 1,512
- ATOM: Accurate Tracking by Overlap Maximization1,509
This work proposes a novel tracking architecture, consisting of dedicated target estimation and classification components, and introduces a classification component that is trained online to guarantee high discriminative power in the presence of distractors.
- Learning Discriminative Model Prediction for Tracking1,436
An end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction, derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations.
- Discriminative Scale Space Tracking1,312
This paper proposes a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation in a tracking-by-detection framework that obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state of theart tracker on VOT2014.
- 975
- 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.
- 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.
- Probabilistic Regression for Visual Tracking750
This work proposes a probabilistic regression formulation and applies it to tracking, which is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task and substantially improves the performance.
- 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.
- 496
- Unveiling the Power of Deep Tracking484
This paper proposes a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy in generic object tracking.
- SRFlow: Learning the Super-Resolution Space with Normalizing Flow444
The proposed SRFlow is a normalizing flow based super-resolution method capable of learning the conditional distribution of the output given the low-resolution input, and directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.
- 434
- Transforming Model Prediction for Tracking433
This work trains the proposed tracker end-to-end and validate its performance by conducting comprehensive experiments on multiple tracking datasets, achieving an AUC of 68.5% on the challenging LaSOT [14] dataset.
- Learning the Model Update for Siamese Trackers431
This work uses a convolutional neural network, called UpdateNet, which given the initial template, the accumulated template and the template of the current frame aims to estimate the optimal template for the next frame, and demonstrates the generality of the proposed approach by applying it to two Siamese trackers.
- Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking430
This work proposes a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks by minimizing a single loss over both the target appearance model and the sample quality weights.
- Know Your Surroundings: Exploiting Scene Information for Object Tracking402
This work proposes a novel tracking architecture which can utilize scene information as dense localized state vectors, which can encode, for example, if the local region is target, background, or distractor and combined with the appearance model output to localize the target.
- Learning Target Candidate Association to Keep Track of What Not to Track388
This work proposes a training strategy that combines partial annotations with self-supervision to keep track of distractor objects in order to continue tracking the target, and introduces a learned association network to propagate the identities of all target candidates from frame-to-frame.
- Deep Projective 3D Semantic Segmentation366
This work states that semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications that requires voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption.
- Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision360
This work proposes a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning and applies this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the- art scalable methods: ensembling and MC-dropout.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
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 2026-10-13. 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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