Deep Learning Technique for Human Parsing: A Survey and Outlook (2301.00394v2)

Abstract: Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.

• The paper presents a comprehensive survey of single, multiple, and video human parsing, leveraging attention mechanisms and multi-task learning for refined segmentation.
• It evaluates various datasets and metrics, addressing challenges such as real-time inference and the high cost of accurate annotations.
• The study outlines promising future directions, including transformer-based baselines and integrated panoptic parsing for enhanced human-centric vision systems.

Deep Learning Techniques for Human Parsing: A Survey and Outlook

The paper "Deep Learning Technique for Human Parsing: A Survey and Outlook" critically examines the landscape of human parsing methodologies, specifically focusing on deep learning paradigms that target the segmentation of human elements in images and videos at the pixel level. The work is a comprehensive exploration of the tasks involved in human parsing, including single human parsing, multiple human parsing, and video human parsing, providing a detailed analysis of existing frameworks, evaluation metrics, challenges, and prospective research directions.

Core Contributions

The authors structure the survey around three primary sub-tasks within human parsing:

1. Single Human Parsing (SHP): This task involves isolating and labeling various parts of a single human figure in an image. It often employs strategies such as context learning, structured representation, and multi-task learning. Techniques in attention mechanisms and scale-aware features highlight the importance of extracting comprehensive contextual information from images. Structured representation techniques, including the use of tree and graph structures, aim at modeling relationships between human body parts. Furthermore, multi-task learning approaches often integrate edge detection and pose estimation to refine human part segmentation.
2. Multiple Human Parsing (MHP): MHP addresses the challenge of identifying and parsing multiple human subjects in a scene. Here, the paper categorizes approaches into bottom-up methods, which deal with semantic segmentation followed by instance grouping, and top-down methods, involving pre-detection of human instances followed by part segmentation.
3. Video Human Parsing (VHP): This segment is devoted to parsing human instances in videos, accommodating the temporal dimension to ensure consistency across frames. Learning temporal correspondences is a key challenge, with methods leveraging cycle-tracking, reconstructive learning, and contrastive learning to propagate semantically accurate labels throughout video sequences.

Evaluation and Methodologies

The paper also offers an extensive evaluation of influential datasets, detailing their scale, annotation granularity, and applicability to various parsing tasks. Additionally, it reviews evaluation metrics like pixel accuracy, mean IoU, AP^r, and AP^p, which serve as benchmarks for parser performance from both semantic and instance perspectives.

Performance comparisons within the survey capture the nuances of methodological advancements over the years, revealing a trend towards incorporating complex network architectures and more refined learning paradigms to boost segmentation accuracy and efficiency.

Challenges and Open Issues

One fundamental issue identified in the current landscape of human parsing is efficient inference, particularly when parsing needs to be executed in real-time across multiple human instances. The paper urges further exploration into the generation of synthetic datasets to alleviate the challenges associated with annotation costs and the generalization to real-world applications, which often suffer from long-tailed distributions.

Moreover, the challenge of interpretability in parsing methodologies is underlined as an area ripe for development, with the potential to enhance trust and reliability in human-centric vision systems.

Future Directions and Conclusion

The paper presents a forward-looking proposition for a transformer-based baseline for human parsing, leveraging recent breakthroughs in mask classification and sequence modeling to enhance prediction accuracy and adaptability across diverse parsing tasks.

The paper suggests investigating new directions such as video instance-level human parsing, panoptic parts parsing, and whole-body human parsing integrating hand and facial elements alongside traditional body parts. The evolving landscape of foundation models, especially with the advent of large-scale vision models like DINO and SAM, presents both challenges and opportunities to redefine the paradigms of human parsing.

In conclusion, "Deep Learning Technique for Human Parsing: A Survey and Outlook" is not only a documentation of historic and current trends but also a blueprint for future research endeavors, encouraging innovative solutions and cross-disciplinary approaches to advance the field of human parsing in the era of artificial intelligence.