Summary of Modelling Visual Semantics Via Image Captioning to Extract Enhanced Multi-level Cross-modal Semantic Incongruity Representation with Attention For Multimodal Sarcasm Detection, by Sajal Aggarwal et al.
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection
by Sajal Aggarwal, Ananya Pandey, Dinesh Kumar Vishwakarma
First submitted to arxiv on: 5 Aug 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 The novel framework for multimodal sarcasm detection presented in this study combines text, images, and image captions to accurately capture the discrepancies between these modalities that are fundamental to detecting sarcasm. The primary contributions include a robust textual feature extraction branch using a cross-lingual language model, a visual feature extraction branch incorporating a self-regulated residual ConvNet with a lightweight spatially aware attention module, and additional modality in image captions generated using an encoder-decoder architecture. The proposed model achieves the best accuracy of 92.89% and 64.48%, respectively, on the Twitter multimodal sarcasm and MultiBully datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to detect sarcasm in social media uses a combination of text, images, and captions. This helps catch when someone is being sarcastic by looking at how the words, pictures, and descriptions don’t match up. The researchers created a special system that can do this well. It has two parts: one for understanding written language and another for understanding images. They also used image captions to help figure out when something doesn’t add up. This new approach is better than previous methods at recognizing sarcasm. |
Keywords
» Artificial intelligence » Attention » Encoder decoder » Feature extraction » Language model
