5 accepted papers
We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate. Modern approaches handle these axes one at a time. In contrast, our proposed model called STARnet super-resolves jointly i…
Visual localization is a crucial requirement in mobile robotics, field and service robotics, and self-driving cars. Recently, unsupervised learning with Slow Feature Analysis (SFA) has shown to produce spatial representations that enable localization from holistic images. The approach is faster and
We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the targe…
In outdoor scenarios changing conditions (e.g., seasonal, weather and lighting effects) have a substantial impact on the appearance of a scene, which often prevents successful visual localization. The application of an unsupervised Slow Feature Analysis (SFA) on the images captured by an autonomous…
The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images…