Summary of Towards Retrieval-augmented Architectures For Image Captioning, by Sara Sarto et al.
Towards Retrieval-Augmented Architectures for Image Captioning
by Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi, Rita Cucchiara
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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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 approach to image captioning models utilizes an external kNN memory to improve the generation process. Two model variants are presented, incorporating a knowledge retriever component based on visual similarities, a differentiable encoder to represent input images, and a kNN-augmented language model to predict tokens. The approach is experimentally validated on COCO and nocaps datasets, demonstrating significant enhancement of caption quality with larger retrieval corpus. This work provides insights into retrieval-augmented captioning models and opens up avenues for improving image captioning at scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers talk about pictures better. It’s like a translator that connects what we see in an image to words that describe it. The researchers came up with a new way to make this happen by using a special memory that can retrieve information from past images. They tested their idea on two big datasets and found that it works really well, especially when they have more information to draw from. This discovery can help us create better tools for understanding what’s in an image. |
Keywords
» Artificial intelligence » Encoder » Image captioning » Language model
