Search
Results
-
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VConference Paper -
GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences
(2020)Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addressed with specialized network architectures, designed for only one particular task. This severely limits the generalization ...Conference Paper -
Learning What to Learn for Video Object Segmentation
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined by a first-frame reference mask during inference. The problem of how to capture and utilize this limited information to accurately segment the target remains a fundamental research question. We address this by introducing an end-to-end trainable VOS architecture that integrates a differentiable few-shot learner. Our learner is designed ...Conference Paper -
Know Your Surroundings: Exploiting Scene Information for Object Tracking
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Current state-of-the-art trackers rely only 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. fast appearance changes or presence of distractor objects, where a target appearance model alone is insufficient for robust tracking. Having the knowledge about the presence and locations of other objects in the surrounding scene can be highly beneficial in such ...Conference Paper -
The Seventh Visual Object Tracking VOT2019 Challenge Results
(2019)2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)Conference Paper -
Video Object Segmentation with Episodic Graph Memory Networks
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IIConference Paper -
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the design of the overall architecture, we investigate a design space that is usually overlooked, i.e. adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by shrinking widened baseline networks and leads to superior performance. Based on that, we articulate the "heterogeneity hypothesis": ...Conference Paper -
Learning Accurate Dense Correspondences and When to Trust Them
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference Paper -
Deep Burst Super-Resolution
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Conference Paper -
Local Memory Attention for Fast Video Semantic Segmentation
(2021)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)We propose a novel neural network module that transforms an existing single-frame semantic segmentation model into a video semantic segmentation pipeline. In contrast to prior works, we strive towards a simple, fast, and general module that can be integrated into virtually any single-frame architecture. Our approach aggregates a rich representation of the semantic information in past frames into a memory module. Information stored in the ...Conference Paper