Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation

• URL: http://arxiv.org/abs/2311.18572v1
• Date: Thu, 30 Nov 2023 14:06:27 GMT
• Title: Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation
• Authors: Avijit Dasgupta and C. V. Jawahar and Karteek Alahari
• Abstract summary: We present a self-training based source-free video domain adaptation approach. We use the source pre-trained model to generate pseudo-labels for the target domain samples. We further enhance the adaptation performance by implementing a teacher-student framework.
• Score: 39.71690595469969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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