Renovating Parsing R-CNN for Accurate Multiple Human Parsing

Abstract: Multiple human parsing aims to segment various human parts and associate each part with the corresponding instance simultaneously. This is a very challenging task due to the diverse human appearance, semantic ambiguity of different body parts, and complex background. Through analysis of multiple human parsing task, we observe that human-centric global perception and accurate instance-level parsing scoring are crucial for obtaining high-quality results. But the most state-of-the-art methods have not paid enough attention to these issues. To reverse this phenomenon, we present Renovating Parsing R-CNN (RP R-CNN), which introduces a global semantic enhanced feature pyramid network and a parsing re-scoring network into the existing high-performance pipeline. The proposed RP R-CNN adopts global semantic representation to enhance multi-scale features for generating human parsing maps, and regresses a confidence score to represent its quality. Extensive experiments show that RP R-CNN performs favorably against state-of-the-art methods on CIHP and MHP-v2 datasets. Code and models are available at https://github.com/soeaver/RP-R-CNN.

• The paper presents RP R-CNN, which integrates a Global Semantic Enhanced FPN and a Parsing Re-Scoring Network to boost parsing accuracy.
• It improves performance by achieving a 2.0-point mIoU gain and enhanced precision metrics on challenging datasets.
• The framework offers efficient, context-aware segmentation for complex human imagery, benefiting applications like VR and action recognition.

Overview of "Renovating Parsing R-CNN for Accurate Multiple Human Parsing"

The paper "Renovating Parsing R-CNN for Accurate Multiple Human Parsing" introduces significant enhancements to the Parsing R-CNN framework, addressing key challenges in multiple human parsing - particularly, the need for global semantic awareness and accurate quality assessment of parsing maps. The authors propose the Renovating Parsing R-CNN (RP R-CNN), which effectively integrates global semantic information and improves map scoring precision within a two-stage top-down parsing approach.

Technical Contributions

1. Global Semantic Enhanced Feature Pyramid Network (GSE-FPN): The proposed GSE-FPN refines standard FPN by incorporating a global semantic enhancement mechanism. The network augments multi-scale features with global contextual information, which is vital for parsing nuanced human details and distinguishing between overlapping instances. This modification bridges the gap left by traditional methods that lack holistic scene understanding.
2. Parsing Re-Scoring Network (PRSN): To reliably evaluate the quality of the parsing outputs, the authors introduce PRSN. This component predicts a confidence score reflecting the quality of instance parsing maps, effectively decoupling this score from the bounding-box detection confidence. This separation allows the network to more accurately signal the parsing quality, addressing a notable deficiency in preceding methods.
3. Implementation and Inference Details: The design of RP R-CNN is mindful of computational efficiency while maximizing accuracy. The inference phase combines global segmentation with instance-level results, yielding comprehensive parsing outputs. This strategy leverages the strengths of various segmentation perspectives.

Experimental Validation

The effectiveness of RP R-CNN is substantiated through experiments on CIHP and MHP-v2 datasets, two challenging benchmarks for human parsing. Compared to state-of-the-art alternatives, RP R-CNN demonstrates superior performance by clear margins across multiple metrics—most notably yielding a 2.0-point improvement in mIoU and substantial gains in precision metrics such as AP$^\text{p}_\text{50}$. These improvements underscore the network's enhanced ability to work with complex human imagery, including small or occluded body parts.

Implications and Future Directions

The advancements presented in this paper hold significant implications for tasks reliant on accurate human parsing, such as human-object interaction modeling, virtual reality simulations, and advanced action recognition systems. By achieving finer segmentation resolution and more reliable performance indicators, RP R-CNN enables more nuanced and reliable human-centric analyses.

Looking forward, the integration of more sophisticated semantic reasoning modules could further bolster parsing accuracy in even more dynamic and cluttered environments. Additionally, extensions of this approach could focus on optimizing real-time processing capabilities, which are crucial for applications in autonomous systems and live video analytics.

In conclusion, the paper presents a significant stride toward more accurate and contextually aware human parsing methodologies, with RP R-CNN offering a robust foundation for continued research and application in complex human-centric image understanding.