Summary of Interclip-mep: Interactive Clip and Memory-enhanced Predictor For Multi-modal Sarcasm Detection, by Junjie Chen et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

by Junjie Chen, Hang Yu, Subin Huang, Sanmin Liu, Linfeng Zhang

First submitted to arxiv on: 24 Jun 2024

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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 A novel approach to detecting sarcasm in social media, InterCLIP-MEP combines Interactive CLIP and a Memory-Enhanced Predictor to extract enriched text-image representations and improve robustness. By embedding cross-modal information into each encoder, InterCLIP-MEP overcomes reliance on spurious cues, achieving state-of-the-art performance on MMSD and MMSD2.0 benchmarks with significant accuracy and F1 score improvements.
Low	GrooveSquid.com (original content)	Low Difficulty Summary A team of researchers created a new way to tell if people are being sarcastic online. They used a combination of computer vision and natural language processing to better understand how sarcasm works in social media posts that have both text and images. This method, called InterCLIP-MEP, is really good at figuring out when someone is being sarcastic. It even beats the current best methods for doing this.