T o address the performance limitations of the Segment Anything Model (SAM) in specific domains, existing works primarily adopt adapter-based one-step adaptation paradigms. However, some of these methods are specific developed for specific domains. If used on other domains may lead to performance degradation. This problem of catastrophic forgetting severely limits the model's scalability. T o address this issue, this paper proposes RegCL, a novel non-replay continual learning (CL) framework designed for efficient multi-domain knowledge integration through model merging. Specifically, RegCL incorporates the model merging algorithm into the continual learning paradigm by merging the parameters of SAM's adaptation modules (e.g., LoRA modules) trained on different domains. The merging process is guided by weight optimization, which minimizes prediction discrepancies between the merged model and each of the domain-specific models. RegCL effectively consolidates multi-domain knowledge while maintaining parameter efficiency, i.e., the model size remains constant regardless of the number of tasks, and no historical data storage is required. Experimental results demonstrate that RegCL achieves favorable continual learning performance across multiple downstream datasets, validating its effectiveness in dynamic scenarios.

With the historical text data, images data or speech data, we can build an application that will help to understand the historical terms more effectively and will also broad line the visuals if needed. Using Natural Language Processing techniques like named entity recognition, part-of-speech tagging we can aim for text summarization with the clear perspective of explaining the historical terms. The report can be generated which could be further utilized for analysis for specific incident or event. During learning history, I felt hard to pronounce the names of kingdom and rulers. Thus, we can apply, listen and speak button for difficult words in the document.