Summary of Flexcap: Describe Anything in Images in Controllable Detail, by Debidatta Dwibedi et al.
FlexCap: Describe Anything in Images in Controllable Detail
by Debidatta Dwibedi, Vidhi Jain, Jonathan Tompson, Andrew Zisserman, Yusuf Aytar
First submitted to arxiv on: 18 Mar 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 introduces FlexCap, a vision-language model that generates region-specific descriptions of varying lengths. Trained to produce length-conditioned captions for input boxes, FlexCap enables control over information density and produces descriptions ranging from concise object labels to detailed captions. The authors create large-scale training datasets of image region descriptions with varying lengths from captioned web images and demonstrate FlexCap’s effectiveness in dense captioning tasks on the Visual Genome dataset. Additionally, they show how FlexCap’s localized descriptions can serve as input to a large language model to create a visual question answering (VQA) system, achieving state-of-the-art zero-shot performance on multiple VQA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlexCap is a new way for computers to describe what they see in images. It takes an image and breaks it into smaller parts, writing a short or long description of each part. The computer is trained using lots of examples of how to write these descriptions, and then it can use those skills to help with tasks like labeling objects, recognizing object attributes, and even answering questions about what’s in the picture. |
Keywords
* Artificial intelligence * Language model * Large language model * Question answering * Zero shot
