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 within conventional frameworks. However, these solutions not only require cumbersome training procedures or additional modal inputs, but also lack generalization capabilities. To address this problem, we propose a unified optical flow estimation framework specifically designed for degraded scenes. In this work, we employ large-scale pre-trained optical flow foundation models as both teacher and student networks. Our objective is to compensate for feature incompleteness during image degradation through pre-trained large models. Subsequently, we leverage supervised signals for fine-tuning and introduce an intra-inter frame distillation method to enable the student network to adapt to diverse cross-domain scenarios. Our proposed methodology provides deeper insights into learning style-invariant features from these learnable fine-tuning layers. Extensive experiments demonstrate that our approach achieves superior generalization performance and state-of-the-art results in degraded scenes (including low-light, rain, fog and other conditions) while requiring minimal training resources.

Egocentric action recognition holds critical value in augmented reality, embodied AI, and human behavior analysis. While transformer-based masked autoencoders show potential in general video representation learning, their direct application to egocentric vision faces fundamental limitations --- random masking strategies disrupt crucial spatiotemporal features like hand-object interaction hierarchies and viewpoint dynamics by neglecting task-specific semantic priors. Through systematic analysis, this paper reveals three complementary semantic priors for egocentric video understanding: verb-centric motion patterns characterizing hand trajectories, noun-aware attention regions highlighting object contact points, and action-oriented global context integrating holistic semantics. These hierarchical cues address egocentric visual specificity through motion granularity, interaction locality, and semantic integrity. Building on this discovery, we propose EgoHierMask: a hierarchical semantic prior-guided masked autoencoder framework coordinating vision-language knowledge through differentiated masking strategies. The framework employs frozen vision-language teacher models to generate multi-level semantic attention maps, systematically guiding three specialized masking branches: a) dynamic motion masking preserves hand movement continuity through temporal verb attention, b) interaction-sensitive masking maintains object manipulation coherence via spatial noun saliency, and c) spatiotemporal joint masking encodes complete action semantics through global context alignment. Additionally, to enhance learning efficacy, we curate a distribution-balanced pretraining corpus and devise a unified architecture with dual-granularity supervision, combining pixel-level reconstruction with semantic-level distillation within the VideoMAE paradigm.Extensive experiments demonstrate state-of-the-art performance across major benchmarks, validating the crucial value of hierarchical prior injection for egocentric representation learning.