{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interclip-mep-interactive-clip-and-memory","title":"InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection","arxiv_id":"2406.16464","date":"2024-06-24","proceeding":null,"authors":["Junjie Chen","Hang Yu","Subin Huang","Sanmin Liu","Linfeng Zhang"],"abstract":"Sarcasm in social media, often expressed through text-image combinations, poses challenges for sentiment analysis and intention mining. Current multi-modal sarcasm detection methods have been demonstrated to overly rely on spurious cues within the textual modality, revealing a limited ability to genuinely identify sarcasm through nuanced text-image interactions. To solve this problem, we propose InterCLIP-MEP, which introduces Interactive CLIP (InterCLIP) with an efficient training strategy to extract enriched text-image representations by embedding cross-modal information directly into each encoder. Additionally, we design a Memory-Enhanced Predictor (MEP) with a dynamic dual-channel memory that stores valuable test sample knowledge during inference, acting as a non-parametric classifier for robust sarcasm recognition. Experiments on two benchmarks demonstrate that InterCLIP-MEP achieves state-of-the-art performance, with significant accuracy and F1 score improvements on MMSD and MMSD2.0. Our code is available at https://github.com/CoderChen01/InterCLIP-MEP.","url_abs":"https://arxiv.org/abs/2406.16464v5","url_pdf":"https://arxiv.org/pdf/2406.16464v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interclip-mep-interactive-clip-and-memory","repo_url":"https://github.com/CoderChen01/InterCLIP-MEP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sarcasm-detection","task_name":"Sarcasm Detection"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}