FluidRain: Incompressible Rain Flow as an Attention Bias for Loop-in-Loop Video Deraining

arXiv:2609.29006 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

Benchmark — 15× in this paper · explained in 2 episodes
Alignment — 14× in this paper · explained in 1 episode
Transformer — 13× in this paper · explained in 2 episodes
Aggregation — 9× in this paper · explained in 1 episode
Robustness — 7× in this paper · explained in 5 episodes
Optical Flow — 3× in this paper · explained in 2 episodes
Consistency — 2× in this paper · explained in 3 episodes
Diffusion Model — 2× in this paper · explained in 8 episodes
Diffusion Models — 2× in this paper · explained in 6 episodes
Generalization — 2× in this paper · explained in 13 episodes
Ablation — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Adaptation — 1× in this paper · explained in 1 episode
Generative AI — 1× in this paper · explained in 2 episodes
Guided attention — 1× in this paper · explained in 2 episodes
Iterative Refinement — 1× in this paper · explained in 3 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Matrix Completion — 1× in this paper · explained in 4 episodes
Redundancy — 1× in this paper · explained in 2 episodes
Regularization — 1× in this paper · explained in 3 episodes
Scaling Laws — 1× in this paper · explained in 4 episodes
Semi-supervised Learning — 1× in this paper · explained in 4 episodes
Trace — 1× in this paper · explained in 2 episodes

Sources

How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models

Related papers