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 …

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 …

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

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 …

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

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 …

We propose a flow-guided transformer, which innovatively leverage the motion discrepancy
exposed by optical flows to instruct the attention retrieval in transformer for high fidelity video …

We introduce SEA-RAFT, a more simple, efficient, and accurate RAFT for optical flow.
Compared with RAFT, SEA-RAFT is trained with a new loss (mixture of Laplace). It directly …