Mema: Memory-Augmented Adapter for Enhanced Vision-Language Understanding
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. Specifically, Mema maintains a stateful memory that accumulates hierarchical visual representations across layers, with its evolution conditioned on both query embeddings and step-wise visual ...
| Authors | Ying Liu, Yudong Han, Kean Shi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 14 April 2026 |
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