Summary of Memeclip: Leveraging Clip Representations For Multimodal Meme Classification, by Siddhant Bikram Shah et al.
MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
by Siddhant Bikram Shah, Shuvam Shiwakoti, Maheep Chaudhary, Haohan Wang
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Multimedia (cs.MM)
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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 tackles the challenge of understanding text-embedded images, a complex multimodal problem. Building on previous research in hate speech analysis, this study expands its scope to cover multiple aspects of linguistics: hate, targets of hate, stance, and humor. A novel dataset, PrideMM, is introduced, comprising 5,063 text-embedded images associated with the LGBTQ+ Pride movement. The authors conduct experiments using unimodal and multimodal baseline methods to establish benchmarks for each task. A new framework, MemeCLIP, is proposed for efficient downstream learning, preserving pre-trained CLIP model knowledge. Results show MemeCLIP outperforms previous frameworks on two real-world datasets, and a comparison with zero-shot GPT-4 is made. The study’s limitations are discussed by qualitatively analyzing misclassified samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand pictures that have words in them. This is a hard problem because we need to figure out what the words mean and what the pictures show. Usually, people just look at one aspect of this problem, like hate speech. But this study looks at many aspects: hate, who gets targeted with hate, how someone feels about something, and whether it’s funny or not. They made a special set of pictures with words to help them learn more about this problem. They tested different ways of solving the problem and came up with a new method that works really well. |
Keywords
» Artificial intelligence » Gpt » Zero shot
