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Works297 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.
- 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.
- Learning Spatially Regularized Correlation Filters for Visual Tracking1,984
The proposed SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples, and an optimization strategy is proposed, based on the iterative Gauss-Seidel method, for efficient online learning.
- 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.
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- 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.
- 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.
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- The monogenic signal924
A geometric phase interpretation that is based on the relation between the 1-D analytic signal and the 2-D monogenic signal established by the Radon (1986) transform is introduced.
- The Visual Object Tracking VOT2015 Challenge Results902
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
- 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.
- 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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- 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.
- RoMa: Robust Dense Feature Matching457
This work proposes a robust model, RoMa, leveraging frozen pretrained features from the foundation model DINOv2, and proposes a tailored transformer match decoder that predicts anchor probabilities, which enables it to express multimodality.
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- 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.
- 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.
- 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.
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- DKM: Dense Kernelized Feature Matching for Geometry Estimation301
The proposed dense method, Dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks, and achieves an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC@5° compared to the best previous sparse method and dense method respectively.
- Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition226
This work proposes to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes in traffic sign recognition by using the correlation based matching scheme for Fourier descriptors and a fast cascaded match scheme for enforcing the spatial requirements.
- Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook224
This survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks, and distinguishes and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms.
- Confidence Propagation through CNNs for Guided Sparse Depth Regression223
This paper proposes an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work and introduces the use of output confidence as an auxiliary information to improve the results.
- The Monogenic Scale-Space: A Unifying Approach to Phase-Based Image Processing in Scale-Space221
In this paper, edge detection by a new approach to phase congruency and its relation to amplitude based methods, reconstruction from local amplitude and local phase, and the evaluation of the local frequency are discussed.
- A Generative Appearance Model for End-To-End Video Object Segmentation200
This work proposes a network architecture that learns a powerful representation of the target and background appearance in a single forward pass, and outperforms all published approaches on the large-scale YouTube-VOS dataset.
- Learning What to Learn for Video Object Segmentation182
This work introduces an end-to-end trainable VOS architecture that integrates a differentiable few-shot learning module that is designed to predict a powerful parametric model of the target by minimizing a segmentation error in the first frame.
- Painting-91: a large scale database for computational painting categorization176
It is shown that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases.
- Learning Fast and Robust Target Models for Video Object Segmentation158
This work proposes a novel VOS architecture consisting of two network components, exclusively trained offline, designed to process the coarse scores into high quality segmentation masks, and achieves favorable performance, while operating at higher frame-rates compared to state-of-the-art.
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- Rolling shutter bundle adjustment145
A bundle adjustment (BA) method that obtains accurate structure and motion from rolling shutter video sequences: RSBA, which succeeds in cases where competing methods diverge, and consistently produces more accurate results.
- Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End142
This work proposes a novel approach to identify disturbed measurements in the input by learning an input confidence estimator in a self-supervised manner based on the normalized convolutional neural networks (NCNNs), and proposes a probabilistic version of NCNNs that produces a statistically meaningful uncertainty measure for the final prediction.
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- Image Analysis and Reconstruction using a Wavelet Transform Constructed from a Reducible Representation of the Euclidean Motion Group128
This work considers robust orientation estimates by means of channel representations, where it combines robust orientation estimation and learning of wavelets resulting in an auto-associative processing of orientation features.
- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Coloring Action Recognition in Still Images113
The experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors and that late fusion of color and shape information outperforms other approaches on action recognition.
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- DeDoDe: Detect, Don’t Describe — Describe, Don’t Detect for Local Feature Matching108
This work trains the detector to detect tracks from large-scale SfM and derives a semi-supervised two-view detection objective to expand this set to a desired number of detections, achieving significant gains on multiple geometry benchmarks.
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- Deep motion features for visual tracking90
To the best of the knowledge, this paper is the first to propose fusing appearance information with deep motion features for visual tracking, and it is shown that hand-crafted, deep RGB, and deepmotion features contain complementary information.
- A New Extension of Linear Signal Processing for Estimating Local Properties and Detecting Features87
This paper presents a filter that is isotropic and odd, which means that the whole theory of local phase and amplitude can directly be applied to images and a third local property is obtained which is the local orientation.
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- Non-commutative Hypercomplex Fourier Transforms of Multidimensional Signals72
It turns out that some powerful concepts of one-dimensional signal theory can hardly be carried over to the theory of n-dimensional signals by using the complex Fourier transform.
- Correlating Fourier descriptors of local patches for road sign recognition69
The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours, and demonstrate that correlation-based approach outperforms AFDs and WARP on real data.
- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
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- Optical Flow Estimation from Monogenic Phase61
The paper gives a short and easy to comprehend overview about the theory of the monogenic phase and the formula for the displacement estimation is derived from a series expansion of the phase.
- Density Adaptive Point Set Registration58
The density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications and outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.
- Compact color–texture description for texture classification55
This paper shows that combining multiple texture description methods significantly improves the recognition performance compared to using a single best texture method alone, and proposes to use information theoretic compression approach to compress high-dimensional multi-texture features into a compact heterogeneous texture representation.
- A continuous Formulation of intrinsic Dimension55
It is shown that the intrinsic dimension is spanned by two axes: one axis represents the variance of the spectral energy and one represents the a weighted variance in orientation.
- Balanced Product of Calibrated Experts for Long-Tailed Recognition54
This work takes an analytical approach and extends the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE), which combines a family of experts with different test-time target distributions, generalizing several previous approaches.
- Riesz-Transforms Versus Derivatives: On the Relationship Between the Boundary Tensor and the Energy Tensor49
This paper defines the quadrature-based boundary tensor and the derivative-based gradient energy tensor which exhibit very similar behavior and develops a simple and very efficient integrated feature detection algorithm.
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- Image Alignment for Panorama Stitching in Sparsely Structured Environments45
This paper proposes a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking and shows that the proposed DCF-based methods outperform phase correlation-based approaches on these datasets.
- GET: The Connection Between Monogenic Scale-Space and Gaussian Derivatives45
A new operator which combines advantages of monogenic scale-space and Gaussian scale- space, of the monogenic signal and the structure tensor is proposed, which compares favourably with existing methods.
- α Scale Spaces on a Bounded Domain44
By introducing the notion of (non-dimensional) relative scale in each α scale space, the α scale spaces have exactly the same eigen-modes and can be implemented simultaneously as scale dependent Fourier series.
- Continuous dimensionality characterization of image structures43
A continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field is introduced by making use of a cone structure and barycentric co-ordinates.
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- Graph representation learning for road type classification39
This work presents a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks, and proposes a novel aggregation approach, Graph Attention Isomorphism Network, GAIN 1 .
- Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera39
The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway, using a monocular thermal camera.
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- POI Detection Using Channel Clustering and the 2D Energy Tensor33
An energy tensor is defined which can be considered as a phase invariant extension of the structure tensor and the channel representation is used for robustly clustering the POI information from the first step resulting in sub-pixel accuracy for the localisation of POI.
- Deep motion and appearance cues for visual tracking32
Generic visual tracking is a challenging computer vision problem, with numerous applications, and most existing approaches rely on appearance information by employing either hand-crafted features or de-crowding techniques.
- Coloring Channel Representations for Visual Tracking32
This work has shown that biologically-inspired channel representations have shown to provide promising results in many applications ranging from autonomous driving to visual tracking, and can be alleviated by constructing robust, discriminative and computationally efficient visual features.
- The Structure Multivector32
The structure multivector is derived from the Laplace equation, which also introduces a new viewpoint on scale space and a phase approach for intrinsically 2D structures is derived and applications are presented which make use of the 2D part of the new operator.
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- An Explicit and Compact Coding of Geometric and Structural Information Applied to Stereo Processing.8
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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 2026-10-13. 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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