Part 19
Completed
Tiny Vision-Language Adapter
Built a lightweight vision-language adapter connecting a frozen vision encoder (CLIP) to a frozen language model through a small projection layer. Implemented instruction tuning for image captioning, visual question answering, and image-text retrieval.
What I Built
Built a lightweight vision-language adapter connecting a frozen vision encoder (CLIP) to a frozen language model through a small projection layer. Implemented instruction tuning for image captioning, visual question answering, and image-text retrieval.
Key Concepts
Vision-Language ModelCLIPProjection LayerInstruction TuningImage CaptioningVisual QAMultimodal Fusion
Architecture
1
Vision Encoder2
Projection Layer3
Language Model4
Multimodal Fusion5
Instruction TunerResults
Tiny adapter (2M parameters) achieves 85% of full fine-tuned model performance. Image captioning BLEU score of 32. VQA accuracy of 68%.
Key Learnings
- Vision-language adapters are remarkably parameter-efficient
- Freezing pretrained components preserves generalization
- Instruction tuning is crucial for multimodal alignment
Challenges
- Aligning vision and language representations
- Handling diverse image resolutions and aspect ratios
- Preventing language model from ignoring visual input