Summary of Voldoger: Llm-assisted Datasets For Domain Generalization in Vision-language Tasks, by Juhwan Choi et al.
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks
by Juhwan Choi, Junehyoung Kwon, JungMin Yun, Seunguk Yu, YoungBin Kim
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed VolDoGer dataset is designed for evaluating the domain generalizability of deep learning models in vision-language tasks such as image captioning, visual question answering, and visual entailment. The dataset addresses the limitations of existing datasets by extending LLM-based data annotation techniques to these tasks. The authors evaluated various models, including fine-tuned models and a recent multimodal large language model, on VolDoGer’s tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VolDoGer is a new dataset that helps computers understand images and words better. It has three challenges: writing captions for pictures, answering questions about what you see in an image, and figuring out if something is true or false based on a picture. The dataset was created to help machines learn to do well even with data they’ve never seen before. It’s like teaching a child to recognize dogs and cats, so when it sees a new animal, it can say “oh, I know what that is!” |
Keywords
» Artificial intelligence » Deep learning » Image captioning » Large language model » Question answering
