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Despite the progress seen in classification methods, current approaches for handling videos with distribution-shift in source and
target domains remain source-dependent as they require access
to the source data during the adaptation stage. In this paper, we
present a self-training based source-free video domain adaptation
approach (without bells and whistles) to address this challenge by
bridging the gap between the source and the target domains. We
use the source pre-trained model to generate pseudo-labels for the
target domain samples, which are inevitably noisy. We treat the
problem of source-free video domain adaptation as learning from
noisy labels and argue that the samples with correct pseudo-labels
can help us in adaptation. To this end, we leverage the cross-entropy
loss as an indicator of the correctness of the pseudo-labels, and use
the resulting small-loss samples from the target domain for fine-tuning the model. Extensive experimental evaluations show that
our method, termed as CleanAdapt, achieves ∼ 7% gain over the
source-only model and outperforms the state-of-the-art approaches
on various open datasets.
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