Summary of A Dataset and Framework For Learning State-invariant Object Representations, by Rohan Sarkar et al.
A Dataset and Framework for Learning State-invariant Object Representations
by Rohan Sarkar, Avinash Kak
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Information Retrieval (cs.IR); 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 enhance object representation learning by introducing state invariance, which enables robustness to changes in structural form. Our novel dataset, ObjectsWithStateChange, captures state and pose variations in objects from arbitrary viewpoints. This dataset facilitates research on fine-grained object recognition and retrieval of 3D objects undergoing state changes. The goal is to train models that learn discriminative embeddings invariant to state changes and viewpoint transformations. To address the challenge of visually similar objects under different states, we propose a curriculum learning strategy that progressively selects object pairs with smaller inter-object distances in the learned embedding space during training. Our ablation study indicates an improvement of 7.9% in object recognition accuracy and 9.2% in retrieval mAP over state-of-the-art models on our new dataset and three other challenging multi-view datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes objects easier to recognize and retrieve from different angles or states. For example, imagine recognizing a book as the same object whether it’s open or closed. To do this, we created a special dataset with pictures of objects in different states. We also developed a way to train machines to learn what objects look like while ignoring changes in their shape. This helps machines recognize objects even when they’re moving or being manipulated. Our approach is better than previous methods at recognizing and retrieving 3D objects that change shape, such as toys or clothes. |
Keywords
» Artificial intelligence » Curriculum learning » Embedding space » Representation learning
