  Despite the progress seen in classification methods, current approaches for
handling videos with distribution shifts 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 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. Thus, 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. We further enhance the adaptation performance by implementing a
teacher-student framework, in which the teacher, which is updated gradually,
produces reliable pseudo-labels. Meanwhile, the student undergoes fine-tuning
on the target domain videos using these generated pseudo-labels to improve its
performance. Extensive experimental evaluations show that our methods, termed
as CleanAdapt, CleanAdapt + TS, achieve state-of-the-art results, outperforming
the existing approaches on various open datasets. Our source code is publicly
available at https://avijit9.github.io/CleanAdapt.
