Summary of Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers For High-level Image Classification, by Delfina Sol Martinez Pandiani et al.
Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification
by Delfina Sol Martinez Pandiani, Nicolas Lazzari, Valentina Presutti
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 aims to revolutionize automatic high-level image understanding by developing innovative approaches that combine traditional deep vision methods with human-like interpretation skills. The authors leverage situated perceptual knowledge from cultural images to enhance performance and interpretability in abstract concept (AC) image classification. They create the ARTstract Knowledge Graph (AKG), which captures nuanced semantic units from over 14,000 labeled cultural images. The AKG is then enriched with high-level linguistic frames and used to compute knowledge graph embeddings and experiment with hybrid approaches that fuse these embeddings with visual transformer embeddings. To ensure interpretability, posthoc qualitative analyses are conducted to examine model similarities with training instances. The results show that the hybrid KGE-ViT methods outperform existing techniques in AC image classification, while posthoc analysis reveals the strengths of each approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand pictures using computers. Imagine you’re looking at an image and trying to figure out what’s going on. This is hard for computers because they don’t have the same experience as humans do. To solve this problem, researchers created a special kind of map called the ARTstract Knowledge Graph (AKG). This map helps computers understand pictures better by giving them more information about what’s happening in the image. The AKG was made using over 14,000 labeled images and includes words that describe the scene. The researchers then tested their method with different computer models to see which one worked best. They found that a combination of two methods was the most successful. This new way of understanding pictures could be useful for many applications, such as helping computers analyze medical images or understand natural language. |
Keywords
» Artificial intelligence » Image classification » Knowledge graph » Transformer » Vit
