Identity-Guided Human Semantic Parsing for Person Re-Identification
By Kuan Zhu, Haiyun Guo, Zhiwei Liu, Ming Tang, Jinqiao Wang. European Conference on Computer Vision, 2020
388 citations recorded by the source corpus.
Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpacks and reticule) which are crucial to person re-ID. In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels. We design the cascaded clustering on feature maps to generate the pseudo-labels of human parts. Specifically, for the pixels of all images of a person, we first group them to foreground or background and then group the foreground pixels to human parts. The cluster assignments are subsequently used as pseudo-labels of human parts to supervise the part estimation and ISP iteratively learns the feature maps and groups them. Finally, local features of both human body parts and personal belongings are obtained according to the selflearned part estimation, and only features of visible parts are utilized for the retrieval. Extensive experiments on three widely used datasets validate the superiority of ISP over lots of state-of-the-art methods. Our code is available at this https URL.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/267ebbabf19e7f67aae6359be42ee2e12479ba17
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
Citations in the corpus
- CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels (2022, background): “…2021b; He et al. 2019; Zhang et al. 2019; Sun et al. 2020) with the purpose of mining rich features from different locations are also proposed, and they can be improved if the semantic parsing map participates during training (Jin et al. 2020b; Zhu et al. 2020; Meng et al. 2020; Chen et al. 2020).”