no code implementations • ICCV 2023 • Goutam Bhat, Michaël Gharbi, Jiawen Chen, Luc van Gool, Zhihao Xia
Extensive experiments on real and synthetic data show that, despite only using noisy bursts during training, models trained with our self-supervised strategy match, and sometimes surpass, the quality of fully-supervised baselines trained with synthetic data or weakly-paired ground-truth.
no code implementations • 10 Oct 2022 • Yihang She, Goutam Bhat, Martin Danelljan, Fisher Yu
These approaches however suffer from ``catastrophic forgetting'' issue due to finetuning of base detector, leading to sub-optimal performance on the base classes.
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 #5 on Visual Object Tracking on AVisT
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.
Ranked #6 on Burst Image Super-Resolution on BurstSR
no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou
This paper reviews the NTIRE2021 challenge on burst super-resolution.
3 code implementations • CVPR 2021 • Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte
We propose a novel architecture for the burst super-resolution task.
Ranked #8 on Burst Image Super-Resolution on SyntheticBurst
Burst Image Super-Resolution Multi-Frame Super-Resolution +1
1 code implementation • ICCV 2021 • Bin Zhao, Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte
This effectively limits the performance and generalization capabilities of existing video segmentation methods.
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.
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.
Ranked #8 on Object Tracking on FE108
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 Object Detection on COCO test-dev (Hardware Burden metric)
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.
Ranked #5 on Object Tracking on FE108
4 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.
Ranked #7 on Object Tracking on FE108
no code implementations • ECCV 2018 • Goutam Bhat, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
In the field of generic object tracking numerous attempts have been made to exploit deep features.
1 code implementation • 9 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.
Ranked #15 on Semantic Segmentation on Semantic3D
5 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.
Ranked #13 on Visual Object Tracking on VOT2017/18