Summary of Weakly Supervised Deep Hyperspherical Quantization For Image Retrieval, by Jinpeng Wang et al.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
by Jinpeng Wang, Bin Chen, Qiang Zhang, Zaiqiao Meng, Shangsong Liang, Shu-Tao Xia
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Deep learning models for large-scale image retrieval have been highly efficient when using ground-truth information, but these methods struggle in scenarios where label information is limited. To address this challenge, researchers propose Weakly-Supervised Deep Hyperspherical Quantization (WSDHQ), a novel approach to learn deep quantization from weakly tagged images. WSDHQ uses word embeddings to enhance the semantic information of informal tags and jointly learns semantics-preserving embeddings and supervised quantizer on hypersphere. The model achieves state-of-the-art performance on weakly-supervised compact coding tasks, demonstrating its effectiveness in learning from noisy labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep quantization methods are great for big image searches! But they need exact labels to work well. What if we only have a little bit of information about each picture? That’s where WSDHQ comes in. It’s a new way to teach deep models using words and tags that aren’t perfect, but still help us learn. By combining word meanings with the images themselves, WSDHQ can make really good predictions even when labels are uncertain. |
Keywords
» Artificial intelligence » Deep learning » Quantization » Semantics » Supervised
