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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.
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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.
- 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.
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- 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.
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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.
- 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.
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- 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.
- Video Object Segmentation with Episodic Graph Memory Networks303
This work exploits an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges and yields a neat yet principled framework, which can generalize well both one-shot and zero-shot video object segmentation tasks.
- GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences262
This work proposes a universal network architecture that is directly applicable to all the aforementioned dense correspondence problems, and achieves both high accuracy and robustness to large displacements by investigating the combined use of global and local correlation layers.
- Learning Human-Object Interaction Detection Using Interaction Points256
This paper proposes a novel fully-convolutional approach that directly detects the interactions between human-object pairs and predicts interaction points, which directly localize and classify the inter-action.
- 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.
- Multi-Modal Fusion for End-to-End RGB-T Tracking219
This work proposes an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking, and evaluates the effectiveness of modality fusion in each of the main components in DiMP, i.e. feature extractor, target estimation network, and classifier.
- 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.
- Unsupervised Learning for Real-World Super-Resolution195
This work learns to invert the effects of bicubic downsampling in order to restore the natural image characteristics present in the data, and can be trained with direct pixel-wise supervision in the high resolution domain, while robustly generalizing to real input.
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- NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results191
The NTIRE 2020 challenge addresses the real world setting, where paired true high and low-resolution images are unavailable, and the ultimate goal is to achieve the best perceptual quality, evaluated using a human study.
- 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.
- Deep Burst Super-Resolution176
A novel architecture for the burst super-resolution task, which takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output by explicitly aligning deep embeddings of the input frames using pixel-wise optical flow.
- Mask Transfiner for High-Quality Instance Segmentation173
This paper presents Mask Transfiner, a transformer-based approach to instance segmentation that decomposes and represents the image regions as a quadtree, allowing it to predict highly accurate instance masks, at a low computational cost.
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- Learning Accurate Dense Correspondences and When to Trust Them171
This work aims to estimate a dense flow field relating two images, coupled with a robust pixel-wise confidence map indicating the reliability and accuracy of the prediction, and develops a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty.
- 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.
- Efficient Visual Tracking with Exemplar Transformers157
The Exemplar Transformer is introduced, a transformer module utilizing a single instance level attention layer for realtime visual object tracking that consistently outperforms all other methods on the LaSOT, OTB-100, NFS, TrackingNet, and VOTST2020 datasets.
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- PDC-Net+: Enhanced Probabilistic Dense Correspondence Network135
The Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, is proposed, capable of estimating accurate dense correspondences along with a reliable confidence map, and a flexible probabilistic approach that jointly learns the flow prediction and its uncertainty is developed.
- Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling131
The hierarchical conditional flow (HCFlow) is proposed as a unified framework for image SR and image rescaling and achieves state-of-the-art performance in terms of both quantitative metrics and visual quality.
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- 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.
- Segment Anything Meets Point Tracking119
This paper introduces SAM-PT, an innovative method for point-centric video segmentation, leveraging the capabilities of SAM alongside long-term point tracking, and uniquely exploits point propagation to utilize local structure information independent of object semantics.
- AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results110
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
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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.
- Learnable Online Graph Representations for 3D Multi-Object Tracking89
The approach provides a natural way for track initialization and handling of false positive detections, while significantly improving track stability, and employs a Neural Message Passing network for data association that is fully trainable.
- OVTrack: Open-Vocabulary Multiple Object Tracking86
OVTrack is developed, an open-vocabulary tracker that is capable of tracking arbitrary object classes and its design is based on two key ingredients, leveraging vision-language models for both classification and association via knowledge distillation and a data hallucination strategy for robust appearance feature learning from denoising diffusion probabilistic models.
- Matching Anything by Segmenting Anything83
This work proposes MASA, a novel method for robust instance association learning, capable of matching any objects within videos across diverse domains without tracking labels, Leveraging the rich object segmentation from the Segment Anything Model (SAM).
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- Deep Reparametrization of Multi-Frame Super-Resolution and Denoising80
The deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks is proposed, derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space.
- Energy-Based Models for Deep Probabilistic Regression76
This work creates an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y), which is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling.
- Cascade-DETR: Delving into High-Quality Universal Object Detection69
Cascade-DETR is introduced for high-quality universal object detection, and the Cascade Attention layer is proposed, which explicitly integrates object-centric information into the detection decoder by limiting the attention to the previous box prediction.
- 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.
- Warp Consistency for Unsupervised Learning of Dense Correspondences61
W warp consistency loss is proposed, an unsupervised learning objective for dense correspondence regression that is effective even in settings with large appearance and view-point changes and sets a new state of the art on several challenging benchmarks, including MegaDepth, RobotCar and TSS.
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- R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras58
This work proposes R3D3, a multi-camera system for dense 3D reconstruction and ego-motion estimation that integrates multi- camera feature correlation and dense bundle adjustment operators that yield robust geometric depth and pose estimates, and introduces learnable scene priors via a depth refinement network.
- 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.
- DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows55
DeFlow, a method for learning stochastic image degradations from unpaired data based on a novel unpaired learning formulation for conditional normalizing flows, is proposed, which allows to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions.
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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.
- NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results43
The NTIRE2021 challenge on burst super-resolution was to generate a clean RGB image with 4 times higher resolution given a RAW noisy burst as input, and the top-performing methods set a new state-of-the-art for the burstsuper-resolution task.
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- Local Memory Attention for Fast Video Semantic Segmentation42
A novel neural network module is proposed that transforms an existing single-frame semantic segmentation model into a video semantic segmentsation pipeline and integrates into two popular semantic segmentations networks: ERFNet and PSPNet.
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- Mask-Free Video Instance Segmentation32
This work proposes MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state, by introducing the Temporal KNN-patch Loss (TK-Loss), providing strong mask supervision without any labels.
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- 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.
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- The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures21
This paper investigates a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks, and finds that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance.
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- The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture.6
The ``heterogeneity hypothesis'' is articulate: with the same training protocol, there exists a layer-wise dissimilated network architecture (LW-DNA) that can outperform the original network with regular channel configurations under lower level of model complexity.
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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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