This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural
network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built …

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes
from a single video. Our learned representation enables free-viewpoint rendering of the …

Optical flow, which captures motion information across frames, is exploited in recent video
inpainting methods through propagating pixels along its trajectories. However, the hand …

We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to
previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently …

Digital video stabilization is a challenging task that aims to transform a potentially shaky
video into a pleasant one by smoothing the camera trajectory. Despite the various works …

FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

Abstract Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene
representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable …

Flow-based propagation and spatiotemporal Transformer are two mainstream mechanisms
in video inpainting (VI). Despite the effectiveness of these components, they still suffer from …

Optical flow estimation is a challenging problem remaining unsolved. Recent deep learning
based optical flow models have achieved considerable success. However, these models …