Summary of Progressive Alignment with Vlm-llm Feature to Augment Defect Classification For the Ase Dataset, by Chih-chung Hsu et al.
Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset
by Chih-Chung Hsu, Chia-Ming Lee, Chun-Hung Sun, Kuang-Ming Wu
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper tackles two long-standing challenges in traditional defect classification approaches: insufficient training data and unstable data quality, as well as over-reliance on visual modalities. The researchers investigate how to address these issues simultaneously by exploring alternative features within datasets and combining vision-language models (VLMs) with large language models (LLMs). The authors propose a novel ASE dataset containing rich data descriptions recorded on images, which is challenging to learn directly. They also introduce prompting for VLM-LLM against defect classification to activate extra-modality features from images and enhance performance. Furthermore, the paper presents a progressive feature alignment (PFA) block to refine image-text features and alleviate difficulties under few-shot scenarios. Finally, the authors design a Cross-modality attention fusion (CMAF) module to effectively fuse different modality features. The experiment results demonstrate the effectiveness of the proposed method over several defect classification methods for the ASE dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve traditional defect classification approaches by addressing two main challenges: insufficient training data and unstable quality, as well as relying too heavily on visual information. By combining language models with image analysis, researchers can create more accurate systems that work even when images are poor quality or difficult to understand. |
Keywords
* Artificial intelligence * Alignment * Attention * Classification * Few shot * Prompting
