Summary of Rethinking Misalignment in Vision-language Model Adaptation From a Causal Perspective, by Yanan Zhang et al.
Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective
by Yanan Zhang, Jiangmeng Li, Lixiang Liu, Wenwen Qiang
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 paper investigates the limitations of Vision-Language models like CLIP when adapting to specific tasks. It identifies a two-level misalignment issue: task misalignment and data misalignment. Soft prompt tuning has improved task alignment, but data misalignment remains a challenge. The authors develop a structural causal model to analyze the impact of data misalignment on prediction results. They find that task-irrelevant knowledge affects predictions and hinders modeling of true relationships between images and classes. To mitigate this issue, they propose Causality-Guided Semantic Decoupling and Classification (CDC), which decouples semantics in downstream tasks and employs Dempster-Shafer evidence theory to evaluate prediction uncertainty. The authors demonstrate the effectiveness of CDC in multiple settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Vision-Language models work when used for specific tasks. They found that these models have a problem called data misalignment, which means they’re not always accurate because they learn from unrelated information. To fix this issue, the authors developed a new method called Causality-Guided Semantic Decoupling and Classification (CDC). CDC helps the model focus on the right information for each task and ignore irrelevant details. The results show that CDC makes the models more accurate. |
Keywords
» Artificial intelligence » Alignment » Classification » Prompt » Semantics
