Catastrophic Forgetting and METEOR Decline in Sequential Multilingual CLIP Adaptation

This report synthesises findings from 8 peer-reviewed papers addressing the following research question: To what extent does catastrophic forgetting affect METEOR scores in multilingual CLIP models when sequentially adapted to non-English video datasets like MSVD-Indonesian. Vision-language modeling (VLM) aims to bridge the information gap between images and natural language. Under the new paradigm of first pretraining on massive image-text pairs and then fine-tuning on task-specific data, VLM in the remote sensing domain has made significant. 14 claims were extracted from source literature; 12 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent does catastrophic forgetting affect METEOR scores in multilingual CLIP models when sequentially adapted to non-English video datasets like MSVD-Indonesian?

Autonomous literature synthesis. Automated review score: 7.9/10. Full text and citation available at Assignee Research.