Summary of A Textgcn-based Decoding Approach For Improving Remote Sensing Image Captioning, by Swadhin Das and Raksha Sharma
A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning
by Swadhin Das, Raksha Sharma
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a novel approach for automatically captioning remote sensing images, which are crucial for addressing complex issues like risk management, security, and meteorology. The proposed method combines Text Graph Convolutional Network (TextGCN) and multi-layer LSTMs in an encoder-decoder setup to capture semantic relationships among words. A comparison-based beam search method is used to ensure fairness in generating captions. The approach is evaluated across three datasets using seven metrics: BLEU-1 to BLEU-4, METEOR, ROUGE-L, and CIDEr. Results show that the proposed method significantly outperforms other state-of-the-art encoder-decoder methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Remote sensing images are important for many reasons, like keeping us safe or helping us predict the weather. But it’s hard to describe what’s in these pictures because they’re very specific and need special knowledge. This paper shows a new way to automatically add words to remote sensing images. It uses two main parts: Text Graph Convolutional Network (TextGCN) and multi-layer LSTMs. This helps the computer understand how words are related. The paper also has a special way to make sure the computer generates good captions. They tested their approach on three groups of pictures using seven different ways to measure how well it worked. It did really well compared to other computers that can do similar things. |
Keywords
» Artificial intelligence » Bleu » Convolutional network » Encoder decoder » Rouge
