Search Results for author: Martin Danelljan

Found 72 papers, 48 papers with code

Arbitrary-Scale Image Synthesis

1 code implementation5 Apr 2022 Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc van Gool

Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales.

Image Generation

Robust Visual Tracking by Segmentation

1 code implementation21 Mar 2022 Matthieu Paul, Martin Danelljan, Christoph Mayer, Luc van Gool

We infer a bounding box from the segmentation mask and validate our tracker on challenging tracking datasets and achieve the new state of the art on LaSOT with a success AUC score of 69. 7%.

Semantic Segmentation Video Object Segmentation +3

Transforming Model Prediction for Tracking

1 code implementation21 Mar 2022 Christoph Mayer, Martin Danelljan, Goutam Bhat, Matthieu Paul, Danda Pani Paudel, Fisher Yu, Luc van Gool

Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function.

 Ranked #1 on Visual Object Tracking on LaSOT (IS metric)

Frame Visual Object Tracking

Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild

no code implementations20 Mar 2022 Ardhendu Shekhar Tripathi, Martin Danelljan, Samarth Shukla, Radu Timofte, Luc van Gool

We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone.

Motion Estimation

Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences

1 code implementation8 Mar 2022 Prune Truong, Martin Danelljan, Fisher Yu, Luc van Gool

We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching.

Adiabatic Quantum Computing for Multi Object Tracking

no code implementations17 Feb 2022 Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc van Gool

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.

Multi-Object Tracking

Fast Online Video Super-Resolution with Deformable Attention Pyramid

no code implementations3 Feb 2022 Dario Fuoli, Martin Danelljan, Radu Timofte, Luc van Gool

Our DAP aligns and integrates information from the recurrent state into the current frame prediction.

Frame Video Super-Resolution

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

no code implementations24 Jan 2022 Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc van Gool

In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.

Denoising Image Inpainting

Collapse by Conditioning: Training Class-conditional GANs with Limited Data

1 code implementation ICLR 2022 Mohamad Shahbazi, Martin Danelljan, Danda Pani Paudel, Luc van Gool

On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.

Efficient Visual Tracking with Exemplar Transformers

1 code implementation17 Dec 2021 Philippe Blatter, Menelaos Kanakis, Martin Danelljan, Luc van Gool

In this paper, we introduce the Exemplar Transformer, an efficient transformer for real-time visual object tracking.

Visual Object Tracking Visual Tracking

Mask Transfiner for High-Quality Instance Segmentation

1 code implementation26 Nov 2021 Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu

Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree.

Instance Segmentation Semantic Segmentation

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

no code implementations5 Nov 2021 Andreas Lugmayr, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions.

Super-Resolution

Learning Proposals for Practical Energy-Based Regression

1 code implementation22 Oct 2021 Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression.

Dense Gaussian Processes for Few-Shot Segmentation

1 code implementation7 Oct 2021 Joakim Johnander, Johan Edstedt, Michael Felsberg, Fahad Shahbaz Khan, Martin Danelljan

The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context.

Gaussian Processes

PDC-Net+: Enhanced Probabilistic Dense Correspondence Network

1 code implementation28 Sep 2021 Prune Truong, Martin Danelljan, Radu Timofte, Luc van Gool

In order to apply dense methods to real-world applications, such as pose estimation, image manipulation, or 3D reconstruction, it is therefore crucial to estimate the confidence of the predicted matches.

3D Reconstruction Geometric Matching +5

TADA: Taxonomy Adaptive Domain Adaptation

no code implementations10 Sep 2021 Rui Gong, Martin Danelljan, Dengxin Dai, Wenguan Wang, Danda Pani Paudel, Ajad Chhatkuli, Fisher Yu, Luc van Gool

We extensively evaluate the effectiveness of our framework under different TADA settings: open taxonomy, coarse-to-fine taxonomy, and partially-overlapping taxonomy.

Contrastive Learning Domain Adaptation

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

2 code implementations ICCV 2021 Goutam Bhat, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.

Denoising Frame +2

Learnable Online Graph Representations for 3D Multi-Object Tracking

no code implementations23 Apr 2021 Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Martin Danelljan, Luc van Gool

Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality.

3D Multi-Object Tracking Autonomous Driving

Warp Consistency for Unsupervised Learning of Dense Correspondences

1 code implementation ICCV 2021 Prune Truong, Martin Danelljan, Fisher Yu, Luc van Gool

From our observations and empirical results, we design a general unsupervised objective employing two of the derived constraints.

Dense Pixel Correspondence Estimation

Deep Gaussian Processes for Few-Shot Segmentation

no code implementations30 Mar 2021 Joakim Johnander, Johan Edstedt, Martin Danelljan, Michael Felsberg, Fahad Shahbaz Khan

Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space.

Gaussian Processes

Learning Target Candidate Association to Keep Track of What Not to Track

1 code implementation ICCV 2021 Christoph Mayer, Martin Danelljan, Danda Pani Paudel, Luc van Gool

To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision.

Frame Visual Object Tracking +1

Local Memory Attention for Fast Video Semantic Segmentation

1 code implementation5 Jan 2021 Matthieu Paul, Martin Danelljan, Luc van Gool, Radu Timofte

Our approach aggregates a rich representation of the semantic information in past frames into a memory module.

Frame Semantic Segmentation +1

Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU

1 code implementation ICCV 2021 Shipra Jain, Danda Paudel Pani, Martin Danelljan, Luc van Gool

In this paper, we propose a novel training methodology to train and scale the existing semantic segmentation models for a large number of semantic classes without increasing the memory overhead.

Image Classification Object Detection +1

Accurate 3D Object Detection using Energy-Based Models

1 code implementation8 Dec 2020 Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

On the KITTI dataset, our proposed approach consistently outperforms the SA-SSD baseline across all 3DOD metrics, demonstrating the potential of EBM-based regression for highly accurate 3DOD.

2D object detection 3D Object Detection

Learning Video Instance Segmentation with Recurrent Graph Neural Networks

no code implementations7 Dec 2020 Joakim Johnander, Emil Brissman, Martin Danelljan, Michael Felsberg

Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output.

Frame Instance Segmentation +2

Fast Few-Shot Classification by Few-Iteration Meta-Learning

1 code implementation1 Oct 2020 Ardhendu Shekhar Tripathi, Martin Danelljan, Luc van Gool, Radu Timofte

By employing an efficient initialization module and a Steepest Descent based optimization algorithm, our base learner predicts a powerful classifier within only a few iterations.

Classification General Classification +2

Video Object Segmentation with Episodic Graph Memory Networks

1 code implementation ECCV 2020 Xiankai Lu, Wenguan Wang, Martin Danelljan, Tianfei Zhou, Jianbing Shen, Luc van Gool

How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segmentation.

Frame Semantic Segmentation +3

The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

1 code implementation CVPR 2021 Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte

Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.

Image Classification Image Restoration +1

SRFlow: Learning the Super-Resolution Space with Normalizing Flow

4 code implementations ECCV 2020 Andreas Lugmayr, Martin Danelljan, Luc van Gool, Radu Timofte

SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

Image Manipulation Super-Resolution

How to Train Your Energy-Based Model for Regression

1 code implementation4 May 2020 Fredrik K. Gustafsson, Martin Danelljan, Radu Timofte, Thomas B. Schön

While they are commonly employed for generative image modeling, recent work has applied EBMs also for regression tasks, achieving state-of-the-art performance on object detection and visual tracking.

Object Detection Visual Object Tracking +1

Learning Human-Object Interaction Detection using Interaction Points

1 code implementation CVPR 2020 Tiancai Wang, Tong Yang, Martin Danelljan, Fahad Shahbaz Khan, Xiangyu Zhang, Jian Sun

Human-object interaction (HOI) detection strives to localize both the human and an object as well as the identification of complex interactions between them.

Human-Object Interaction Detection Keypoint Detection +1

Probabilistic Regression for Visual Tracking

2 code implementations CVPR 2020 Martin Danelljan, Luc van Gool, Radu Timofte

In this work, we therefore propose a probabilistic regression formulation and apply it to tracking.

Frame Visual Tracking

Learning What to Learn for Video Object Segmentation

2 code implementations ECCV 2020 Goutam Bhat, Felix Järemo Lawin, Martin Danelljan, Andreas Robinson, Michael Felsberg, Luc van Gool, Radu Timofte

This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach.

Few-Shot Learning Frame +4

Know Your Surroundings: Exploiting Scene Information for Object Tracking

1 code implementation ECCV 2020 Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte

Such approaches are however prone to fail in case of e. g. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking.

Frame Object Tracking

GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences

2 code implementations CVPR 2020 Prune Truong, Martin Danelljan, Radu Timofte

Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences.

Dense Pixel Correspondence Estimation Geometric Matching +1

Energy-Based Models for Deep Probabilistic Regression

1 code implementation ECCV 2020 Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat, Thomas B. Schön

In our proposed approach, we create 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).

 Ranked #1 on Visual Object Tracking on TrackingNet (Success Rate metric)

Head Pose Estimation Object Detection +2

Unsupervised Learning for Real-World Super-Resolution

no code implementations20 Sep 2019 Andreas Lugmayr, Martin Danelljan, Radu Timofte

Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image.

Image Super-Resolution

Multi-Modal Fusion for End-to-End RGB-T Tracking

1 code implementation30 Aug 2019 Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia, Joost Van de Weijer, Fahad Shahbaz Khan

Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.

Image-to-Image Translation Rgb-T Tracking

Learning the Model Update for Siamese Trackers

1 code implementation ICCV 2019 Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan

In general, this template is linearly combined with the accumulated template from the previous frame, resulting in an exponential decay of information over time.

Frame Visual Tracking

Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

1 code implementation4 Jun 2019 Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön

We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning.

Depth Completion Semantic Segmentation

Discriminative Online Learning for Fast Video Object Segmentation

no code implementations18 Apr 2019 Andreas Robinson, Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg

We propose a novel approach, based on a dedicated target appearance model that is exclusively learned online to discriminate between the target and background image regions.

Frame One-shot visual object segmentation +4

Learning Discriminative Model Prediction for Tracking

2 code implementations ICCV 2019 Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte

The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking.

online learning Visual Object Tracking +1

ATOM: Accurate Tracking by Overlap Maximization

3 code implementations CVPR 2019 Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg

We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.

General Classification Visual Object Tracking +1

Synthetic data generation for end-to-end thermal infrared tracking

no code implementations4 Jun 2018 Lichao Zhang, Abel Gonzalez-Garcia, Joost Van de Weijer, Martin Danelljan, Fahad Shahbaz Khan

These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking.

Image-to-Image Translation Synthetic Data Generation +2

Density Adaptive Point Set Registration

1 code implementation CVPR 2018 Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssén, Michael Felsberg

Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes.

Deep Projective 3D Semantic Segmentation

1 code implementation9 May 2017 Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg

Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results.

Deep Motion Features for Visual Tracking

no code implementations20 Dec 2016 Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg

To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking.

Action Recognition Optical Flow Estimation +1

ECO: Efficient Convolution Operators for Tracking

2 code implementations CVPR 2017 Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg

Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65. 0% AUC on OTB-2015.

Visual Object Tracking

Discriminative Scale Space Tracking

no code implementations20 Sep 2016 Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg

Compared to the standard exhaustive scale search, our approach achieves a gain of 2. 5% in average overlap precision on the OTB dataset.

Frame Visual Object Tracking

Learning Spatially Regularized Correlation Filters for Visual Tracking

no code implementations ICCV 2015 Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg

These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood.

online learning Visual Tracking

A Probabilistic Framework for Color-Based Point Set Registration

no code implementations CVPR 2016 Martin Danelljan, Giulia Meneghetti, Fahad Shahbaz Khan, Michael Felsberg

On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline.

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