Summary of P4q: Learning to Prompt For Quantization in Visual-language Models, by Huixin Sun et al.
P4Q: Learning to Prompt for Quantization in Visual-language Models
by Huixin Sun, Runqi Wang, Yanjing Li, Xianbin Cao, Xiaolong Jiang, Yao Hu, Baochang Zhang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed “Prompt for Quantization” (P4Q) method balances fine-tuning and quantization to deploy large-scale Vision-Language Models (VLMs) efficiently. P4Q leverages contrastive loss supervision to enhance recognition performance in Post-Training Quantization (PTQ). The approach reorganizes textual representations with learnable prompts, realigning image and text feature distributions. A distillation loss based on cosine similarity predictions further distills the quantized model from a full-precision teacher. Experimental results demonstrate P4Q outperforms prior arts, achieving comparable results to full-precision counterparts while reducing memory requirements. For instance, 8-bit P4Q compresses CLIP-ViT/B-32 by 4x and achieves 66.94% Top-1 accuracy on ImageNet, outperforming a learnable prompt fine-tuned full-precision model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large-scale pre-trained Vision-Language Models (VLMs) have become popular in visual and multimodal tasks, but deploying them remains challenging due to large training sample and computing requirements. Fine-tuning and quantization can reduce these costs. There are two main approaches: Quantization-Aware Training (QAT), which is effective but expensive; and low-bit Post-Training Quantization (PTQ), which has a noticeable performance drop. The new method, “Prompt for Quantization” (P4Q), balances fine-tuning and quantization by designing a lightweight architecture to enhance recognition performance in PTQ. This approach uses learnable prompts to reorganize textual representations and realign image and text features. P4Q can compress models without sacrificing accuracy. |
Keywords
» Artificial intelligence » Contrastive loss » Cosine similarity » Distillation » Fine tuning » Precision » Prompt » Quantization » Vit
