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Learning Target Candidate Association To Keep Track of What Not To Track
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)The presence of objects that are confusingly similar to the tracked target, poses a fundamental challenge in appearance-based visual tracking. Such distractor objects are easily misclassified as the target itself, leading to eventual tracking failure. While most methods strive to suppress distractors through more powerful appearance models, we take an alternative approach.We propose to keep track of distractor objects in order to continue ...Conference Paper -
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)omain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks (such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to each other. However, such a supervision is not always available. In this work, we leverage the guidance from self-supervised ...Conference Paper -
Warp Consistency for Unsupervised Learning of Dense Correspondences
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying on synthetic training pairs often suffer from poor generalisation to real data.We propose Warp Consistency, an unsupervised ...Conference Paper -
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)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 learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image ...Conference Paper -
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation ...Conference Paper -
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accuracy by jointly modelling the downscaling and upscaling processes. While existing approaches employ specialized techniques ...Conference Paper -
Fourier Space Losses for Efficient Perceptual Image Super-Resolution
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Many super-resolution (SR) models are optimized for high performance only and therefore lack efficiency due to large model complexity. As large models are often not practical in real-world applications, we investigate and propose novel loss functions, to enable SR with high perceptual quality from much more efficient models. The representative power for a given low-complexity generator network can only be fully leveraged by strong guidance ...Conference Paper -
Task Switching Network for Multi-task Learning
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We introduce Task Switching Networks (TSNs), a task-conditioned architecture with a single unified encoder/decoder for efficient multi-task learning. Multiple tasks are performed by switching between them, performing one task at a time. TSNs have a constant number of parameters irrespective of the number of tasks. This scalable yet conceptually simple approach circumvents the overhead and intricacy of task-specific network components in ...Conference Paper -
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for ...Conference Paper -
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the ground plane, this corresponds to scene understanding in the bird's-eye-view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding, making this task very challenging. In this work, we ...Conference Paper