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- Bhat, Goutam, et al.
(författare)
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NTIRE 2022 Burst Super-Resolution Challenge
- 2022
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Ingår i: 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022). - : IEEE. - 9781665487399 - 9781665487405 ; , s. 1040-1060
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Konferensbidrag (refereegranskat)abstract
- Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.
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| 2. |
- Gustafsson, Fredrik K., et al.
(författare)
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Learning Proposals for Practical Energy-Based Regression
- 2022
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Ingår i: International conference on artificial intelligence and statistics, vol 151. - : JMLR-JOURNAL MACHINE LEARNING RESEARCH. ; , s. 4685-4704
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Konferensbidrag (refereegranskat)abstract
- Energy-based models (EBMs) have experienced a resurgence within machine learning in recent years, including as a promising alternative for probabilistic regression. However, energy-based regression requires a proposal distribution to be manually designed for training, and an initial estimate has to be provided at test-time. We address both of these issues by introducing a conceptually simple method to automatically learn an effective proposal distribution, which is parameterized by a separate network head. To this end, we derive a surprising result, leading to a unified training objective that jointly minimizes the KL divergence from the proposal to the EBM, and the negative log-likelihood of the EBM. At test-time, we can then employ importance sampling with the trained proposal to efficiently evaluate the learned EBM and produce standalone predictions. Furthermore, we utilize our derived training objective to learn mixture density networks (MDNs) with a jointly trained energy-based teacher, consistently outperforming conventional MDN training on four real-world regression tasks within computer vision. Code is available at https://github.com/fregu856/ebms_proposals.
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| 3. |
- Johnander, Joakim, et al.
(författare)
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Dense Gaussian Processes for Few-Shot Segmentation
- 2022
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Ingår i: COMPUTER VISION, ECCV 2022, PT XXIX. - Cham : SPRINGER INTERNATIONAL PUBLISHING AG. - 9783031198175 - 9783031198182 ; , s. 217-234
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Konferensbidrag (refereegranskat)abstract
- 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 detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions. Furthermore, it provides a principled means of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. Instead of a one-dimensional mask output, we further exploit the end-to-end learning capabilities of our approach to learn a high-dimensional output space for the GP. Our approach sets a new state-of-the-art on the PASCAL-5(i) and COCO-20(i) benchmarks, achieving an absolute gain of +8.4 mIoU in the COCO-20(i) 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-dataset transfer.
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| 4. |
- Johnander, Joakim, 1993-
(författare)
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Dynamic Visual Learning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous robots act in a \emph{dynamic} world where both the robots and other objects may move. The surround sensing systems of said robots therefore work with dynamic input data and need to estimate both the current state of the environment as well as its dynamics. One of the key elements to obtain a high-level understanding of the environment is to track dynamic objects. This enables the system to understand what the objects are doing; predict where they will be in the future; and in the future better estimate where they are. In this thesis, I focus on input from visual cameras, images. Images have, with the advent of neural networks, become a cornerstone in sensing systems. Image-processing neural networks are optimized to perform a specific computer vision task -- such as recognizing cats and dogs -- on vast datasets of annotated examples. This is usually referred to as \emph{offline training} and given a well-designed neural network, enough high-quality data, and a suitable offline training formulation, the neural network is expected to become adept at the specific task.This thesis starts with a study of object tracking. The tracking is based on the visual appearance of the object, achieved via discriminative convolution filters (DCFs). The first contribution of this thesis is to decompose the filter into multiple subfilters. This serves to increase the robustness during object deformations or rotations. Moreover, it provides a more fine-grained representation of the object state as the subfilters are expected to roughly track object parts. In the second contribution, a neural network is trained directly for object tracking. In order to obtain a fine-grained representation of the object state, it is represented as a segmentation. The main challenge lies in the design of a neural network able to tackle this task. While the common neural networks excel at recognizing patterns seen during offline training, they struggle to store novel patterns in order to later recognize them. To overcome this limitation, a novel appearance learning mechanism is proposed. The mechanism extends the state-of-the-art and is shown to generalize remarkably well to novel data. In the third contribution, the method is used together with a novel fusion strategy and failure detection criterion to semi-automatically annotate visual and thermal videos.Sensing systems need not only track objects, but also detect them. The fourth contribution of this thesis strives to tackle joint detection, tracking, and segmentation of all objects from a predefined set of object classes. The challenge here lies not only in the neural network design, but also in the design of the offline training formulation. The final approach, a recurrent graph neural network, outperforms prior works that have a runtime of the same order of magnitude.Last, this thesis studies \emph{dynamic} learning of novel visual concepts. It is observed that the learning mechanisms used for object tracking essentially learns the appearance of the tracked object. It is natural to ask whether this appearance learning could be extended beyond individual objects to entire semantic classes, enabling the system to learn new concepts based on just a few training examples. Such an ability is desirable in autonomous systems as it removes the need of manually annotating thousands of examples of each class that needs recognition. Instead, the system is trained to efficiently learn to recognize new classes. In the fifth contribution, we propose a novel learning mechanism based on Gaussian process regression. With this mechanism, our neural network outperforms the state-of-the-art and the performance gap is especially large when multiple training examples are given.To summarize, this thesis studies and makes several contributions to learning systems that parse dynamic visuals and that dynamically learn visual appearances or concepts.
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