Summary of Parameter Hierarchical Optimization For Visible-infrared Person Re-identification, by Zeng Yu and Yunxiao Shi
Parameter Hierarchical Optimization for Visible-Infrared Person Re-Identification
by Zeng YU, Yunxiao Shi
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Parameter Hierarchical Optimization (PHO) method for Visible-infrared Person Re-Identification (VI-ReID) narrows the search space of parameters, making it easier to train the network. The approach involves dividing parameters into different types and introducing a self-adaptive alignment strategy (SAS) to align visible and infrared images through transformation. Additionally, an auto-weighted alignment learning (AAL) module is developed to automatically weight features according to their importance. The PHO method yields better parameter training and outperforms existing VI-reID approaches. The paper also establishes a cross-modality consistent learning (CCL) loss to extract discriminative person representations with translation consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to match pictures of people taken by different cameras that capture visible or infrared light. This is challenging because the images may look very different, but the goal is to find a way to match them correctly. The method involves adjusting some parameters without training the entire network, which makes it more efficient and effective. It also uses special techniques to align the images and weight their features according to importance. The approach outperforms existing methods for this task. |
Keywords
» Artificial intelligence » Alignment » Optimization » Translation
