Search Results for author: Goutam Bhat

Found 15 papers, 11 papers with code

Self-Supervised Burst Super-Resolution

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.


Fast Hierarchical Learning for Few-Shot Object Detection

no code implementations10 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.

Few-Shot Object Detection object-detection +1

Transforming Model Prediction for Tracking

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 #18 on Visual Object Tracking on LaSOT (Precision metric)

Inductive Bias Visual Object Tracking

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.

Burst Image Super-Resolution Denoising +2

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.

Object Tracking

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 Object Detection on COCO test-dev (Hardware Burden metric)

Head Pose Estimation object-detection +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.

Visual Object Tracking Visual Tracking

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

ECO: Efficient Convolution Operators for Tracking

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.

Visual Object Tracking

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