Summary of Demonstrating and Reducing Shortcuts in Vision-language Representation Learning, by Maurits Bleeker et al.
Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning
by Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke
First submitted to arxiv on: 27 Feb 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 This research paper investigates the limitations of contrastive training in vision-language models (VLMs) when dealing with multiple captions per image. The authors propose a novel approach, synthetic shortcuts for vision-language, which injects artificial shortcuts into image-text data to test whether VLMs can learn task-optimal representations or rely on simpler shortcuts to minimize contrastive loss. They demonstrate that VLMs trained from scratch or fine-tuned with these shortcuts mainly focus on learning features representing the shortcut, rather than the actual task-relevant information. To mitigate this issue, the authors introduce two methods: latent target decoding and implicit feature modification. Empirical results show that both approaches improve performance on evaluation tasks but only partially reduce shortcut learning. This study highlights the challenges in developing effective contrastive vision-language representation learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to understand pictures and words together. Usually, we train these computers using a specific method called contrastive training. But what if there are multiple descriptions of the same picture? This can confuse the computer. In this paper, researchers created a new way to test how well these computers learn by introducing fake shortcuts in the data. They found that when given these shortcuts, the computers tend to focus on the shortcut rather than learning about the actual picture and words. To fix this problem, they came up with two new methods: one helps the computer pay attention to the correct information, and the other changes how it processes the data. The results show that these new methods can improve performance but still have some limitations. |
Keywords
» Artificial intelligence » Attention » Contrastive loss » Representation learning
