Summary of Leveraging Chat-based Large Vision Language Models For Multimodal Out-of-context Detection, by Fatma Shalabi et al.
Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection
by Fatma Shalabi, Hichem Felouat, Huy H. Nguyen, Isao Echizen
First submitted to arxiv on: 22 Jan 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 Large vision-language models (LVLMs) excel in various tasks, such as image classification and text generation. However, their proficiency in detecting out-of-context (OOC) images and texts is unclear. This paper investigates the ability of LVLMs to detect multimodal OOC and finds that they struggle without fine-tuning. Surprisingly, fine-tuning these models on multimodal OOC datasets can significantly improve their detection accuracy. To demonstrate this, we fine-tune MiniGPT-4 on the NewsCLIPpings dataset, a large collection of multimodal OOC. Our results show that fine-tuning MiniGPT-4 improves its performance in detecting OOC images and texts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out if an image or piece of text is relevant to what you’re looking at. This is called out-of-context (OOC) detection, and it’s a tough task. Some models are good at other tasks like recognizing pictures or generating words. But can they really do well on OOC detection? Not without some extra training! In this study, scientists found that these special models can get much better at detecting OOC images and texts if you train them specifically for this job. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Text generation
