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

Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few …

Transformer, as a strong and flexible architecture for modelling long-range relations, has
been widely explored in vision tasks. However, when used in video inpainting that requires …

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 a new task--language-driven video inpainting which uses natural language
instructions to guide the inpainting process. This approach overcomes the limitations of …

Physical objects have inertia, which resists changes in the velocity and motion direction.
Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a …

Video inpainting aims to fill spatio-temporal" corrupted" regions with plausible content. To
achieve this goal, it is necessary to find correspondences from neighbouring frames to …

Video inpainting aims to fill the given spatiotemporal holes with realistic appearance but is
still a challenging task even with prosperous deep learning approaches. Recent works …

We propose a novel framework for video inpainting by adopting an internal learning
strategy. Unlike previous methods that use optical flow for cross-frame context propagation …