FAST: Flow Any Scene Transformer

arXiv:2609.39748 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Optical Flow — 30× in this paper · explained in 2 episodes
• Benchmark — 27× in this paper · explained in 2 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Foundation Model — 11× in this paper · explained in 3 episodes
• Adaptation — 9× in this paper · explained in 1 episode
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Attention Mechanism — 4× in this paper · explained in 3 episodes
• Flow Any Scene Transformer — 4× in this paper
• AUC — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Annotation Ambiguity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models
• Depth Anything 3: Recovering the Visual Space from Any Views
• YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
• The Replica Dataset: A Digital Replica of Indoor Spaces
• TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation

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