[Paper Review] Devil in the Details: Towards Accurate Single and Multiple Human Parsing

Tao Ruan, Ting Liu+5 more|arXiv (Cornell University)|Sep 17, 2018

Multimodal Machine Learning Applications35 references67 citations

TL;DR

The paper analyzes key properties for human parsing—feature resolution, global context, and edge details—and proposes CE2P, a simple end-to-end framework that achieves state-of-the-art results on single and multiple human parsing benchmarks, including LIP, CIHP, and MHP v2.0.

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}.

Motivation & Objective

• Identify factors affecting fine-grained human parsing performance (feature resolution, global context, edge details).
• Design a simple, end-to-end framework that leverages these properties for single human parsing.
• Extend the approach to multiple human parsing with an effective fusion strategy and instance-aware refinement.
• Provide a strong, reusable baseline with open-source code for future research in single/multiple human parsing.

Proposed method

• Analyze the impact of three properties (high-resolution embedding, global context embedding, edge perceiving) on mIoU in human parsing.
• Introduce CE2P, comprising: (i) a high-resolution embedding module to recover detail, (ii) a global context embedding module using pyramid pooling, (iii) an edge perceiving module with multi-scale semantic edge branches.
• Train CE2P end-to-end with ResNet-101 as backbone and cross-entropy losses for parsing and edge supervision, plus an edge-parsing joint loss.
• Apply CE2P to multiple human parsing via M-CE2P, combining global parsing with local patches generated by Mask R-CNN and a refinement step to enhance instance-level labels.

Experimental results

Research questions

• RQ1 Can high-resolution features, global context, and edge cues together improve fine-grained human parsing?
• RQ2 How does CE2P perform on single human parsing compared to state-of-the-art methods on LIP?
• RQ3 Can CE2P be extended to multi-person parsing with effective branch fusion and label refinement?
• RQ4 What is the contribution of global vs. local branches in M-CE2P to CIHP and MHP v2.0?
• RQ5 Does the proposed refinement step meaningfully improve instance-level parsing accuracy?

Key findings

• CE2P outperforms state-of-the-art on LIP single-human parsing with mIoU of 53.10% (and 53.10% with flipping, 0.6% gain from test-time augmentation).
• Single-model CE2P achieves 56.50% mIoU on Track 1; with flipping, 60.10%? (Note: use exact from Table 3: 56.50 mIoU for single model; flipping row shows mIoU 60.10 in the text).
• CE2P surpasses prior methods on CIHP (Track 2) with mean APr of 50.94 and APr@0.5 of 45.31; on MHP v2.0 (Track 5) CE2P achieves PCP0.5 of 41.82 and mean APp of 42.25.
• For multiple parsing, M-CE2P with all three branches (B_g, B_l1, B_l2) plus refinement achieves the best metrics (APr0.5, PCP0.5, mean APp) compared with strongest baselines.
• Edge and high-resolution modules provide tangible gains (~1–2% in mIoU; larger gains for small parts like socks, sunglasses).
• Label refinement boosts instance-level parsing performance by addressing under-segmentation from Mask R-CNN masks.