CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models

📰 ArXiv cs.AI

CARPE prioritizes context-aware image representations in large vision-language models to improve vision-centric capabilities

advanced Published 30 Mar 2026

Action Steps

Identify the limitations of large vision-language models in vision-centric tasks
Implement Context-Aware Image Representation Prioritization via Ensemble (CARPE) to align visual representations with linguistic space
Evaluate the performance of CARPE on image classification tasks and compare with base vision encoders
Fine-tune CARPE for specific vision-language model architectures to optimize results

Who Needs to Know This

Computer vision engineers and researchers on a team benefit from CARPE as it enhances the performance of large vision-language models on image classification tasks, while also being relevant to ML researchers and engineers working on multimodal models

Key Insight

💡 Prioritizing context-aware image representations can improve the performance of large vision-language models on image classification tasks

Key Takeaways

CARPE prioritizes context-aware image representations in large vision-language models to improve vision-centric capabilities

Full Article

Title: CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models

Abstract:
arXiv:2601.13622v3 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) are typically trained using autoregressive language modeling objectives, which align visual representations with linguistic space. While effective for multimodal reasoning, this alignment can weaken vision-centric capabilities, causing LVLMs to underperform their base vision encoders on tasks such as image classification. To address this limitation, we propose Context-Aware Image Representation Priorit

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