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Xiaochun Zou

2 accepted papers

2026

FlowAnyTime: Efficient Fine-tuning with Intra-Inter Frame Distillation for All-Weather Optical Flow Estimation

AAAI 2026technical

Motion estimation in degraded scenes has long been a significant challenge, primarily attributed to substantial scene variations and insufficient training data. Existing approaches typically address this limitation by incorporating additional training strategies or modifying network architectures wi

Cited by 0SourcePDFScholar
2026

Prototypical Action Reasoning Facilitated by Vision-Language Alignment for Egocentric Action Anticipation

CVPR 2026

Egocentric Action Anticipation aims to infer future actions from videos, which is crucial for embodied AI systems. However, its advancement is hindered by the inherent stochasticity of the future, which introduces significant prediction uncertainty. Prevailing methods typically adopt an end-to-end a

Cited by 0SourceScholar