Summary of Set2seq Transformer: Learning Permutation Aware Set Representations Of Artistic Sequences, by Athanasios Efthymiou et al.
Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences
by Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 We propose Set2Seq Transformer, a novel sequential multiple instance architecture, which learns to rank permutation-aware set representations of sequences. The paper highlights the limitations of static visual multiple instance learning methods that neglect temporal information. We demonstrate the benefits of end-to-end sequential multiple instance learning, integrating visual content and temporal information in a multimodal manner. As an application, we focus on fine art analysis tasks, such as predicting artistic success. Our Set2Seq Transformer leverages visual set and temporal position-aware representations to model visual artists’ oeuvres. We evaluate our method using the WikiArt-Seq2Rank dataset and a visual learning-to-rank downstream task, showing that it captures essential temporal information, improving performance over strong static and sequential multiple instance learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand art styles by looking at paintings from different time periods. This paper proposes a new way of analyzing these artworks called Set2Seq Transformer. It helps us see how artists changed their style over time, which is important for predicting artistic success. The old way of doing this didn’t work well because it only looked at the visual details of each painting, without considering when it was created. Our new method combines both visual and temporal information to get a better understanding of art history. |
Keywords
* Artificial intelligence * Transformer
