Researchers have developed PACE, a novel two-stage framework designed to improve multimodal embedding models. This framework addresses the limitations of existing cosine-based contrastive objectives by progressively expanding the representation and trainable parameter spaces. PACE initially uses a cosine-based objective with low-rank adaptation for stable angular geometry, then transitions to dot-product similarity and full-parameter fine-tuning to leverage both angular and norm information for richer semantic encoding. The method also incorporates Focal Embedding Loss to adaptively focus on ambiguous queries, demonstrating consistent effectiveness across various tasks and model scales. AI
IMPACT Enhances multimodal embedding models by enabling richer semantic encoding through combined angular and norm information.
RANK_REASON The cluster contains a research paper detailing a new technical framework for multimodal embedding models. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- computer science
- Computer vision and pattern recognition
- DagsHub
- Focal Embedding Loss
- Gotit.pub
- Hugging Face
- Influence Flower
- PACE
- ScienceCast
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