Summary of Ladic: Are Diffusion Models Really Inferior to Autoregressive Counterparts For Image-to-text Generation?, by Yuchi Wang et al.
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation?
by Yuchi Wang, Shuhuai Ren, Rundong Gao, Linli Yao, Qingyan Guo, Kaikai An, Jianhong Bai, Xu Sun
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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 The paper investigates the application of diffusion models for image-to-text generation, specifically captioning. Current Auto-Regressive (AR) models outperform diffusion models in this task, but diffusion models can alleviate AR limitations like slow inference speed and unidirectional constraints. The prior underperformance of diffusion models is attributed to a lack of effective latent space for image-text alignment and the discrepancy between continuous diffusion processes and discrete textual data. To address these issues, the authors introduce LaDiC, an architecture that utilizes a split BERT to create a dedicated latent space for captions, integrates a regularization module for varying text lengths, and includes a diffuser for semantic image-to-text conversion and a Back&Refine technique for token interactivity during inference. LaDiC achieves state-of-the-art performance on the MS COCO dataset with 38.2 BLEU@4 and 126.2 CIDEr, demonstrating competitiveness with AR models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to use a type of AI model called diffusion models to describe images using words. Right now, other types of AI models are better at this task, but the authors think that diffusion models can do it too. They found that previous attempts with diffusion models didn’t work well because they couldn’t connect images and text in the right way. To fix this, they created a new system called LaDiC that uses two parts: one to understand captions and another to convert images into words. This new system works really well on a big dataset of images and captions, beating other AI models at the task. |
Keywords
» Artificial intelligence » Alignment » Bert » Bleu » Diffusion » Inference » Latent space » Regularization » Text generation » Token
