Summary of Vipcap: Retrieval Text-based Visual Prompts For Lightweight Image Captioning, by Taewhan Kim et al.
ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning
by Taewhan Kim, Soeun Lee, Si-Woo Kim, Dong-Jin Kim
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 proposes ViPCap, a novel approach to lightweight image captioning that leverages the retrieved text with image information as visual prompts. Current models only utilize the retrieved text and CLIP visual embedding, which limits their ability to capture relevant visual information. ViPCap addresses this issue by mapping text prompts into the CLIP space and generating multiple randomized Gaussian distributions to retrieve semantic features containing image information. This approach is evaluated on COCO, Flickr30k, and NoCaps datasets, showing significant performance improvements over prior lightweight captioning models in terms of efficiency and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ViPCap is a new way to help machines describe pictures using words. Right now, most machines only use the text they find with an image, which doesn’t capture all the important details about the picture. ViPCap changes this by combining the text with information from the image itself. This makes it better at describing what’s in the picture. The results show that ViPCap does a great job of describing pictures and is more efficient than other methods. |
Keywords
» Artificial intelligence » Embedding » Image captioning
