Computer Science > Computer Vision and Pattern Recognition
arXiv:2412.19289 (cs)
[Submitted on 26 Dec 2024 (v1), last revised 24 Jan 2025 (this version, v3)]
Title:ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning
View a PDF of the paper titled ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning, by Taewhan Kim and 3 other authors
View PDFHTML (experimental)Abstract:Recent lightweight image captioning models using retrieved data mainly focus on text prompts. However, previous works only utilize the retrieved text as text prompts, and the visual information relies only on the CLIP visual embedding. Because of this issue, there is a limitation that the image descriptions inherent in the prompt are not sufficiently reflected in the visual embedding space. To tackle this issue, we propose ViPCap, a novel retrieval text-based visual prompt for lightweight image captioning. ViPCap leverages the retrieved text with image information as visual prompts to enhance the ability of the model to capture relevant visual information. By mapping text prompts into the CLIP space and generating multiple randomized Gaussian distributions, our method leverages sampling to explore randomly augmented distributions and effectively retrieves the semantic features that contain image information. These retrieved features are integrated into the image and designated as the visual prompt, leading to performance improvements on the datasets such as COCO, Flickr30k, and NoCaps. Experimental results demonstrate that ViPCap significantly outperforms prior lightweight captioning models in efficiency and effectiveness, demonstrating the potential for a plug-and-play solution. The source code is available atthis https URL.
| Comments: | Accepted to AAAI 2025 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2412.19289 [cs.CV] |
| (orarXiv:2412.19289v3 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2412.19289 arXiv-issued DOI via DataCite |
Submission history
From: Taewhan Kim [view email][v1] Thu, 26 Dec 2024 17:29:38 UTC (1,507 KB)
[v2] Mon, 30 Dec 2024 05:07:17 UTC (1,507 KB)
[v3] Fri, 24 Jan 2025 16:16:52 UTC (532 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning, by Taewhan Kim and 3 other authors
Current browse context:
cs.CV
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.