Devil in the Details: Towards Accurate Single and Multiple Human Parsing (1809.05996v3)

Abstract: Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we achieved the 1st places on all three human parsing benchmarks. Without any bells and whistles, we achieved 56.50\% (mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and 1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at \url{https://github.com/liutinglt/CE2P}.

• The paper introduces the CE2P framework that fuses high-resolution embedding, global context, and edge perceiving modules to improve human parsing precision.
• The paper demonstrates that CE2P outperforms previous models with significant mIoU and AP improvements across standard human parsing benchmarks.
• The paper establishes a versatile baseline that informs future research on modular approaches for both single and multiple human parsing tasks.

Overview of "Devil in the Details: Towards Accurate Single and Multiple Human Parsing"

The paper "Devil in the Details: Towards Accurate Single and Multiple Human Parsing" thoroughly examines the nuances of human parsing, a significant task in computer vision involving the semantic segmentation of human images into detailed components like clothing and body parts. The work's core contribution is the introduction of the Context Embedding with Edge Perceiving (CE2P) framework, which is specifically designed to enhance the accuracy of human parsing via the integration of multiple key properties.

Key Contributions and Technical Details

The authors identify three essential properties crucial to this task: feature resolution, global context information, and the precision of edge details. They argue that these elements can be harnessed to improve human parsing outcomes and empirically validate their claims through rigorous experimental setups. This leads to the development of CE2P, a framework that utilizes the aforementioned properties to refine segmentation results:

1. High-Resolution Embedding Module: This module ensures that high-resolution details are preserved by embedding fine-grained information from intermediate network layers, compensating for the loss of detail usually caused by down-sampling operations typical in convolutional networks.
2. Global Context Embedding Module: By employing pyramid pooling techniques, this module captures multi-scale contextual information critical for differentiating between visually similar classes, such as left and right shoes or arms, thus enhancing overall semantic understanding.
3. Edge Perceiving Module: This component is designed to incorporate the characteristics of object boundaries, efficiently refining the semantic segmentation boundaries through the perception of edges, thereby enhancing the parsing precision.

The synergy of these modules within the CE2P framework allows for end-to-end trainability, achieving notable performance improvements over state-of-the-art methods in human parsing tasks.

Empirical Results

The CE2P framework demonstrated substantial improvements on multiple benchmarks, achieving first-place results in three tracks of the LIP Challenge. Specifically, it achieved mIoU scores of 56.50%, 45.31% (mean $AP^r$), and 33.34% ($AP^p_{0.5}$) on different benchmarking datasets, surpassing previous best results by over 2.06%, 3.81%, and 1.87%, respectively. Such results were achieved without additional enhancements, highlighting the robustness of the proposed system.

Implications and Future Directions

The introduction of CE2P offers a solid baseline for future human parsing research, demonstrating the efficacy of integrating high-resolution information, global context, and edge detail perception in semantic segmentation frameworks. Its modular structure allows for easy adoption and could inspire further research into modular approaches for other complex vision tasks.

Moving forward, advancements could focus on refining edge perception and global context integration to address challenges posed by occlusions and cluttered backgrounds. Additionally, exploring extensions of the framework to other domains or incorporating real-time processing capabilities could enhance its applicability in diverse fields such as augmented reality and human-computer interaction.

In conclusion, the paper successfully articulates a clear and effective strategy to improve human parsing through an innovative use of detailed properties, reinforcing the significance of detailed feature utilization in advancing computer vision tasks.