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An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Dario Fuoli, Zhiwu Huang, Danda Pani Paudel +2
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the…
Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli +1
Image features for retrieval-based localization must be invariant to dynamic objects (e.g. cars) as well as seasonal and daytime changes. Such invariances are, up to some extent, l…
Automated Search for Resource-Efficient Branched Multi-Task Networks
David Bruggemann, Menelaos Kanakis, Stamatios Georgoulis +1
The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently. Typically, such architectures have been handcraf…
Self-Calibration Supported Robust Projective Structure-from-Motion
Rui Gong, Danda Pani Paudel, Ajad Chhatkuli +1
Typical Structure-from-Motion (SfM) pipelines rely on finding correspondences across images, recovering the projective structure of the observed scene and upgrading it to a metric…
Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
Ren Yang, Fabian Mentzer, Luc Van Gool +1
The past few years have witnessed increasing interests in applying deep learning to video compression. However, the existing approaches compress a video frame with only a few numbe…
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Christos Sakaridis, Dengxin Dai, Luc Van Gool
We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreo…