通过重建来记忆：视频流上的域增量学习与测试时训练
Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams
AI总结：
提出一种结合主任务头和自监督掩码自编码器头的域增量学习方法，通过测试时训练识别最佳LoRA适配器以重新记忆域，适用于视频流数据。
摘要：
在这项工作中，我们提出了一种新颖的域增量学习方法，使模型能够随时间适应不断演变的非平稳数据。与其他工作不同，我们不试图避免灾难性遗忘，而是允许并利用它。我们的模型结合了一个主任务头和一个自监督掩码自编码器（MAE）头。然后在增量训练期间学习特定于域的LoRA适配器。每个适配器专攻其域，自然地在两个头上诱导对其他域的遗忘。在推理时，我们在自监督MAE头上进行在线测试时训练，以识别哪些LoRA最匹配当前输入，从而使模型能够再次“记住”该域。我们的方案特别适用于现实世界的流数据，例如视频，其中连续样本高度相关且域变化是渐进的。我们在域增量动作识别和语义分割任务上展示了我们的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
