Summary of Deep Dependency Networks For Multi-label Classification, by Shivvrat Arya et al.
Deep Dependency Networks for Multi-Label Classification
by Shivvrat Arya, Yu Xiang, Vibhav Gogate
First submitted to arxiv on: 1 Feb 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 a novel approach combining probabilistic graphical models and deep learning for multi-label classification on image and video data. Our method builds upon previous work by incorporating iterative join graph propagation, integer linear programming, and _1 regularization-based structure learning to improve performance. We then introduce deep dependency networks, augmenting a dependency network with a neural network output layer. Our experimental evaluation on six datasets (Charades, TACoS, Wetlab, MS-COCO, PASCAL VOC, and NUS-WIDE) shows that our approach outperforms pure neural architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to solve a problem called multi-label classification. This is important for things like recognizing objects in images or actions in videos. Our method combines two powerful tools: probabilistic graphical models and deep learning. We tested our approach on six different datasets and found that it performs better than other methods. |
Keywords
* Artificial intelligence * Classification * Deep learning * Neural network * Regularization
