AI RESEARCH
Mema: Memory-Augmented Adapter for Enhanced Vision-Language Understanding
arXiv CS.CV
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ArXi:2603.00655v2 Announce Type: replace Multimodal Large Language Models (MLLMs) have achieved remarkable performance by aligning pretrained visual representations with the linguistic knowledge embedded in Large Language Models (LLMs). However, existing approaches typically rely on final-layer visual features or learnable multi-layer fusion, which often fail to sufficiently exploit hierarchical visual cues without explicit cross-layer interaction design. In this work, we propose a Memory-Augmented Adapter (Mema) within the vision encoder.