Summary of Zero-shot Object-centric Representation Learning, by Aniket Didolkar and Andrii Zadaianchuk and Anirudh Goyal and Mike Mozer and Yoshua Bengio and Georg Martius and Maximilian Seitzer
Zero-Shot Object-Centric Representation Learning
by Aniket Didolkar, Andrii Zadaianchuk, Anirudh Goyal, Mike Mozer, Yoshua Bengio, Georg Martius, Maximilian Seitzer
First submitted to arxiv on: 17 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 The paper investigates the limitations of object-centric representation learning methods, which typically require training and evaluation on the same dataset. The authors introduce a benchmark comprising eight synthetic and real-world datasets to study zero-shot generalization. They find that training on diverse real-world images improves transferability to unseen scenarios. To adapt pre-trained vision encoders for object discovery, they propose a novel fine-tuning strategy inspired by task-specific fine-tuning in foundation models. The results show state-of-the-art performance for unsupervised object discovery with strong zero-shot transfer to unseen datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computer vision systems can generalize to new situations without being trained on those exact situations before. They create a test set of eight different types of images and find that training the system on many real-world images helps it do better in new situations. The authors also develop a way to adapt pre-trained systems for a specific task, like finding objects in an image. Their approach leads to the best results so far for this type of problem. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Representation learning » Transferability » Unsupervised » Zero shot
