Summary of Cic-bart-ssa: Controllable Image Captioning with Structured Semantic Augmentation, by Kalliopi Basioti et al.
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation
by Kalliopi Basioti, Mohamed A. Abdelsalam, Federico Fancellu, Vladimir Pavlovic, Afsaneh Fazly
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Controllable Image Captioning (CIC) aims to generate natural language descriptions for an image, conditioned on user-provided information. Existing datasets primarily contain captions describing entire images, limiting their effectiveness for training CIC models that can focus on specific regions or relationships. To address this challenge, the authors propose a novel method using Abstract Meaning Representation (AMR) to encode spatio-semantic relations between entities and augment existing datasets with visually grounded captions. This Structured Semantic Augmentation (SSA) framework increases spatial and semantic diversity in image-caption datasets. The authors develop a new CIC model, CIC-BART-SSA, tailored for the CIC task, using SSA-diversified datasets as control signals. Compared to state-of-the-art (SOTA) CIC models, CIC-BART-SSA generates captions with superior diversity and text quality, competitive controllability, and reduced gap between broad and focused controlled captioning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture and having it describe itself in your own words. This is called Controllable Image Captioning (CIC). But current datasets for this task are limited because they only provide descriptions of the whole image, not specific parts. To fix this, researchers came up with a new way to create more focused captions by using a special kind of language understanding called Abstract Meaning Representation (AMR). They took existing datasets and added these new captions, making them better suited for training CIC models. The result is a new model that can generate captions that are not only diverse but also accurate and easy to control. This breakthrough could lead to more realistic and natural-sounding descriptions of images. |
Keywords
» Artificial intelligence » Image captioning » Language understanding
