Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866916065008680960 |
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| author | Swinnen, Jonathan Tuytelaars, Tinne |
| author_facet | Swinnen, Jonathan Tuytelaars, Tinne |
| contents | In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to other works, we do not attempt to avoid catastrophic forgetting, but rather allow it and exploit it. Our model combines a main task head with a self-supervised masked autoencoder (MAE) head. We then learn domain-specific LoRA adapters during incremental training. Each adapter specializes to its domain, naturally inducing forgetting on other domains in both heads. At inference, we perform online test-time training on the self-supervised MAE head to identify which LoRAs best matches the current input, so the model can `remember' the domain again. Our scheme is especially well-suited to real-world streaming data, such as video, where consecutive samples are highly correlated and domain shifts are gradual. We demonstrate our method on domain-incremental action recognition and semantic segmentation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31108 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams Swinnen, Jonathan Tuytelaars, Tinne Computer Vision and Pattern Recognition Machine Learning In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to other works, we do not attempt to avoid catastrophic forgetting, but rather allow it and exploit it. Our model combines a main task head with a self-supervised masked autoencoder (MAE) head. We then learn domain-specific LoRA adapters during incremental training. Each adapter specializes to its domain, naturally inducing forgetting on other domains in both heads. At inference, we perform online test-time training on the self-supervised MAE head to identify which LoRAs best matches the current input, so the model can `remember' the domain again. Our scheme is especially well-suited to real-world streaming data, such as video, where consecutive samples are highly correlated and domain shifts are gradual. We demonstrate our method on domain-incremental action recognition and semantic segmentation tasks. |
| title | Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.31108 |