  Few-shot action recognition is an emerging field in computer vision,
primarily focused on meta-learning within the same domain. However, challenges
arise in real-world scenario deployment, as gathering extensive labeled data
within a specific domain is laborious and time-intensive. Thus, attention
shifts towards cross-domain few-shot action recognition, requiring the model to
generalize across domains with significant deviations. Therefore, we propose a
novel approach, ``Distillation from Mixed-Source Domain", tailored to address
this conundrum. Our method strategically integrates insights from both labeled
data of the source domain and unlabeled data of the target domain during the
training. The ResNet18 is used as the backbone to extract spatial features from
the source and target domains. We design two branches for meta-training: the
original-source and the mixed-source branches. In the first branch, a Domain
Temporal Encoder is employed to capture temporal features for both the source
and target domains. Additionally, a Domain Temporal Decoder is employed to
reconstruct all extracted features. In the other branch, a Domain Mixed Encoder
is used to handle labeled source domain data and unlabeled target domain data,
generating mixed-source domain features. We incorporate a pre-training stage
before meta-training, featuring a network architecture similar to that of the
first branch. Lastly, we introduce a dual distillation mechanism to refine the
classification probabilities of source domain features, aligning them with
those of mixed-source domain features. This iterative process enriches the
insights of the original-source branch with knowledge from the mixed-source
branch, thereby enhancing the model's generalization capabilities. Our code is
available at URL: \url{https://xxxx/xxxx/xxxx.git}
