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Article
Open AccessRecurrent Graph Neural Networks for Video Instance Segmentation
Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentati...
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Chapter and Conference Paper
The Tenth Visual Object Tracking VOT2022 Challenge Results
The Visual Object Tracking challenge VOT2022 is the tenth annual tracker benchmarking activity organized by the VOT initiative. Results of 93 entries are presented; many are state-of-the-art trackers published...
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Chapter and Conference Paper
Transform Your Smartphone into a DSLR Camera: Learning the ISP in the Wild
We propose a trainable Image Signal Processing (ISP) framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we e...
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Chapter and Conference Paper
Video Mask Transfiner for High-Quality Video Instance Segmentation
While Video Instance Segmentation (VIS) has seen rapid progress, current approaches struggle to predict high-quality masks with accurate boundary details. Moreover, the predicted segmentations often fluctuate ...
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Chapter and Conference Paper
Tracking Every Thing in the Wild
Current multi-category Multiple Object Tracking (MOT) metrics use class labels to group tracking results for per-class evaluation. Similarly, MOT methods typically only associate objects with the same class pr...
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Chapter and Conference Paper
Robust Visual Tracking by Segmentation
Estimating the target extent poses a fundamental challenge in visual object tracking. Typically, trackers are box-centric and fully rely on a bounding box to define the target in the scene. In practice, objects o...
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Chapter and Conference Paper
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation
Traditional domain adaptive semantic segmentation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the input domain gap, the standard d...
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Chapter and Conference Paper
Dense Gaussian Processes for Few-Shot Segmentation
Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates d...
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Chapter and Conference Paper
Video Instance Segmentation with Recurrent Graph Neural Networks
Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentati...
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Chapter and Conference Paper
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Current state-of-the-art trackers rely only on a target appearance model in order to localize the object in each frame. Such approaches are however prone to fail in case of e.g. fast appearance changes or pres...
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Chapter and Conference Paper
Energy-Based Models for Deep Probabilistic Regression
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such techniqu...
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Chapter and Conference Paper
Learning What to Learn for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined by a first-frame reference mask during inference. The problem of how to capture and utilize this limited...
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Chapter and Conference Paper
The Eighth Visual Object Tracking VOT2020 Challenge Results
The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers publish...
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Chapter and Conference Paper
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnificatio...
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Chapter and Conference Paper
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art deep learning based approaches....
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Chapter and Conference Paper
Video Object Segmentation with Episodic Graph Memory Networks
How to make a segmentation model efficiently adapt to a specific video as well as online target appearance variations is a fundamental issue in the field of video object segmentation. In this work, a graph mem...
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Chapter and Conference Paper
The Sixth Visual Object Tracking VOT2018 Challenge Results
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...
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Chapter and Conference Paper
On the Optimization of Advanced DCF-Trackers
Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detecto...
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Chapter and Conference Paper
Unveiling the Power of Deep Tracking
In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to metho...
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Chapter and Conference Paper
DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking
Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. Howe...
