Computer vision for autonomous vehicles: Problems, datasets and state of the art

J Janai, F Güney, A Behl, A Geiger - Foundations and Trends® …, 2020 - nowpublishers.com
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Deep learning for fluid velocity field estimation: A review

C Yu, X Bi, Y Fan - Ocean Engineering, 2023 - Elsevier
Deep learning technique, has made tremendous progress in fluid mechanics in recent
years, because of its mighty feature extraction capacity from complicated and massive fluid …

Learning to estimate hidden motions with global motion aggregation

S Jiang, D Campbell, Y Lu, H Li… - Proceedings of the …, 2021 - openaccess.thecvf.com
Occlusions pose a significant challenge to optical flow algorithms that rely on local
evidences. We consider an occluded point to be one that is imaged in the first frame but not …

Simple unsupervised object-centric learning for complex and naturalistic videos

G Singh, YF Wu, S Ahn - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Unsupervised object-centric learning aims to represent the modular, compositional, and
causal structure of a scene as a set of object representations and thereby promises to …

UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders

J Zhang, DP Fan, Y Dai, S Anwar… - Proceedings of the …, 2020 - openaccess.thecvf.com
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D saliency …

Digging into self-supervised monocular depth estimation

C Godard, O Mac Aodha, M Firman… - Proceedings of the …, 2019 - openaccess.thecvf.com
Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

Softmax splatting for video frame interpolation

S Niklaus, F Liu - Proceedings of the IEEE/CVF conference …, 2020 - openaccess.thecvf.com
Differentiable image sampling in the form of backward warping has seen broad adoption in
tasks like depth estimation and optical flow prediction. In contrast, how to perform forward …

Xvfi: extreme video frame interpolation

H Sim, J Oh, M Kim - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the
extreme motion to the research community for video frame interpolation (VFI), and propose …

Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras

A Gordon, H Li, R Jonschkowski… - Proceedings of the …, 2019 - openaccess.thecvf.com
We present a novel method for simultaneous learning of depth, egomotion, object motion,
and camera intrinsics from monocular videos, using only consistency across neighboring …

Separable flow: Learning motion cost volumes for optical flow estimation

F Zhang, OJ Woodford… - Proceedings of the …, 2021 - openaccess.thecvf.com
Full-motion cost volumes play a central role in current state-of-the-art optical flow methods.
However, constructed using simple feature correlations, they lack the ability to encapsulate …