#domain-adaptation2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago2

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See, Act, Adapt: Active Perception for Unsupervised Cross-Domain Visual Adaptation via Personalized VLM-Guided Agent

Researchers introduce Sea² (See, Act, Adapt), a novel approach that improves AI perception models in new environments by using an intelligent pose-control agent rather than retraining the models themselves. The method keeps perception modules frozen and uses a vision-language model as a controller, achieving significant performance improvements of 13-27% across visual tasks without requiring additional training data.

AIBullisharXiv – CS AI · 4h ago4

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FineScope : SAE-guided Data Selection Enables Domain Specific LLM Pruning and Finetuning

Researchers introduce FineScope, a framework that uses Sparse Autoencoder (SAE) techniques to create smaller, domain-specific language models from larger pretrained LLMs through structured pruning and self-data distillation. The method achieves competitive performance while significantly reducing computational requirements compared to training from scratch.