Summary of Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations For Recognition and Retrieval, by Rohan Sarkar et al.
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval
by Rohan Sarkar, Avinash Kak
First submitted to arxiv on: 1 Mar 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 This paper presents a novel attention-based dual-encoder architecture for pose-invariant object recognition and retrieval. The authors demonstrate that simultaneously learning category-based and object-identity-based embeddings during training can lead to significant performance improvements. This approach is achieved through specially designed loss functions that optimize inter- and intra-class distances in two separate embedding spaces. The proposed method outperforms previous state-of-the-art methods by 20% on ModelNet40, 2% on ObjectPI, and 46.5% on FG3D for single-view object recognition, and by 33.7% on ModelNet40, 18.8% on ObjectPI, and 56.9% on FG3D for single-view object retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that learning about categories and objects at the same time makes a big difference in recognizing and finding objects. The authors created a new way to train machines using special kinds of math problems that make sure the machine understands both what kind of thing it is seeing (like a car or a dog) and which specific object it is (like a red sports car or a fluffy golden retriever). They tested their method on lots of different pictures and found that it worked way better than other methods. |
Keywords
* Artificial intelligence * Attention * Encoder
