no code implementations • 22 Dec 2022 • Christoph Mayer, Martin Danelljan, Ming-Hsuan Yang, Vittorio Ferrari, Luc van Gool, Alina Kuznetsova
TaMOs achieves a 4x faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark.
1 code implementation • 14 Aug 2022 • Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Akshay Dudhane, Martin Danelljan, Hisham Cholakkal, Salman Khan, Luc van Gool, Fahad Shahbaz Khan
While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects.
1 code implementation • 21 Mar 2022 • Matthieu Paul, Martin Danelljan, Christoph Mayer, Luc van Gool
We infer a bounding box from the segmentation mask, 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%.
1 code implementation • CVPR 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)
no code implementations • 29 Sep 2021 • Adrian Lang, Christoph Mayer, Radu Timofte
We emphasize aspects such as the importance of using data augmentation, the need of separating the contribution of a classification network and the acquisition strategy to the overall performance, the advantages that a proper initialization of the network can bring to AL.
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.
Ranked #2 on Visual Object Tracking on OTB-2015
2 code implementations • CVPR 2020 • Yawei Li, Shuhang Gu, Christoph Mayer, Luc van Gool, Radu Timofte
In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.
no code implementations • 26 Dec 2019 • Matthieu Paul, Christoph Mayer, Luc van Gool, Radu Timofte
(ii) On the GPU, two Convolutional Neural Networks: A main segmentation network that is used to predict dense semantic labels from scratch, and a Refiner that is designed to improve predictions from previous frames with the help of a fast Inconsistencies Attention Module (IAM).
no code implementations • 22 Dec 2019 • Christoph Mayer, Matthieu Paul, Radu Timofte
We test our method on CIFAR10 and SVHN.
no code implementations • ICLR 2019 • Christoph Mayer, Radu Timofte
This paper proposes asal, a new GAN based active learning method that generates high entropy samples.
no code implementations • 5 Aug 2018 • Christoph Mayer, Radu Timofte, Grégory Paul
We reduce the gap by 64. 2% whereas the current state-of-the-art reduces it only by 57. 5%.
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation