23 citations · 47 across the 6 of their papers we have counts for
14 papers
Fast Hierarchical Learning for Few-Shot Object Detection
Yihang She, Goutam Bhat, Martin Danelljan +1
Transfer learning based approaches have recently achieved promising results on the few-shot detection task. These approaches however suffer from ``catastrophic forgetting'' issue d…
Transforming Model Prediction for Tracking
Christoph Mayer, Martin Danelljan, Goutam Bhat +4
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective fun…
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
Goutam Bhat, Martin Danelljan, Fisher Yu +2
We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learn…
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte +25
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
Bin Zhao, Goutam Bhat, Martin Danelljan +2
Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets…
Deep Burst Super-Resolution
Goutam Bhat, Martin Danelljan, Luc Van Gool +1
While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high fre…
Learning What to Learn for Video Object Segmentation
Goutam Bhat, Felix Järemo Lawin, Martin Danelljan +4
Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of ho…
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Goutam Bhat, Martin Danelljan, Luc Van Gool +1
Current state-of-the-art trackers only rely 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. fa…
Energy-Based Models for Deep Probabilistic Regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat +1
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one parti…
Learning Discriminative Model Prediction for Tracking
Goutam Bhat, Martin Danelljan, Luc Van Gool +1
The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, trackin…
ATOM: Accurate Tracking by Overlap Maximization
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan +1
While recent years have witnessed astonishing improvements in visual tracking robustness, the advancements in tracking accuracy have been limited. As the focus has been directed to…
Unveiling the Power of Deep Tracking
Goutam Bhat, Joakim Johnander, Martin Danelljan +2
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…
Deep Projective 3D Semantic Segmentation
Felix Järemo Lawin, Martin Danelljan, Patrik Tosteberg +3
Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmenta…
ECO: Efficient Convolution Operators for Tracking
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan +1
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing trac…