Summary of Wrim-net: Wide-ranging Information Mining Network For Visible-infrared Person Re-identification, by Yonggan Wu et al.
WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification
by Yonggan Wu, Ling-Chao Meng, Yuan Zichao, Sixian Chan, Hong-Qiang Wang
First submitted to arxiv on: 20 Aug 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 paper introduces the Wide-Ranging Information Mining Network (WRIM-Net) for visible-infrared person re-identification (VI-ReID), which addresses the challenge of cross-modality discrepancy by extracting modality-invariant information across a wide range. WRIM-Net consists of a Multi-dimension Interactive Information Mining (MIIM) module and an Auxiliary-Information-based Contrastive Learning (AICL) approach, empowered by Global Region Interaction (GRI). The MIIM module mines non-local spatial and channel information through intra-dimension interaction, while AICL guides the network in extracting modality-invariant information using the Cross-Modality Key-Instance Contrastive (CMKIC) loss. WRIM-Net outperforms state-of-the-art methods on the SYSU-MM01, RegDB, and LLCM datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a problem called cross-modality discrepancy in person re-identification, which is important because it makes it harder to recognize people from different types of images. The authors created a new way to look at information that’s hidden across different kinds of pictures. They call this method the Wide-Ranging Information Mining Network (WRIM-Net). WRIM-Net has two main parts: one that looks at different pieces of information in an image and another that helps the network learn what’s important from comparing different types of images. |
