Deep Human Parsing with Active Template Regression (1503.02391v1)

Abstract: In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an Active Template Regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches $64.38\%$ by our ATR framework, significantly higher than $44.76\%$ based on the state-of-the-art algorithm.

• The paper introduces Active Template Regression (ATR), a novel deep learning framework that models human parsing by predicting mask templates and shape parameters using a two-network architecture.
• The ATR framework significantly outperforms state-of-the-art methods in human parsing accuracy, achieving an F1-score of 64.38% on a large dataset compared to 44.76% from previous approaches, while also being efficient.
• This top-down approach provides theoretical advancements beyond pixel-level methods and supports practical applications like fashion image analysis, virtual try-on, and human-computer interaction requiring precise segmentation.

Overview of 'Deep Human Parsing with Active Template Regression'

The paper "Deep Human Parsing with Active Template Regression" presents a novel approach to the human parsing problem, which involves segmenting a human image into semantic fashion and body regions. The authors propose an innovative Active Template Regression (ATR) framework, which leverages deep Convolutional Neural Networks (CNNs) to estimate the structure outputs required for accurate human parsing.

Key Contributions

1. ATR Formulation: The authors model human parsing as an ATR problem. This approach departs from traditional pixel-level or region hypothesis classification, instead focusing on predicting and morphing masks of each label. This method embodies a top-down approach, directly addressing the diversity and complexity of human appearance and poses.
2. Two-Network Architecture:
   • The Active Template Network predicts mask template coefficients, realizing a linear combination of learned mask templates. This network preserves contextual correlations among label masks.
   • The Active Shape Network predicts the active shape parameters (position, scale, visibility), eschewing max-pooling layers to retain spatial sensitivity crucial for accurate shape regression.
3. Superiority in Performance: The ATR framework outperforms state-of-the-art methods, particularly in F1-scores. On a large dataset, the ATR method achieves an F1-score of 64.38%, a substantial improvement over 44.76% reported with existing approaches.

Technical Details and Results

• CNN Infrastructure: The networks are built on architectures that achieve effective regression of structure outputs. The active shape network notably omits max-pooling to maintain positional awareness.
• Template Dictionary Learning: Non-negative Matrix Factorization (NMF) is employed to learn part-based template dictionaries for each semantic label. This step ensures robustness in capturing intra-class variations in apparel and body parts.
• Efficiency and Scalability: The ATR framework achieves rapid processing times (~0.5 seconds per image on a GPU), signifying its potential for real-time applications.

Implications

The implications of this work are notable both in theory and practice:

• Theoretical Implications: By directly predicting higher-order structure (template coefficients and shape parameters), the ATR framework advances beyond traditional segmentation paradigms that rely heavily on low-level cues.
• Practical Applications: The method inherently supports applications like fashion image analysis, virtual try-on systems, and human-computer interaction interfaces where precise parsing is crucial.

Future Directions

Potential avenues for future exploration include extending the ATR approach to generic image segmentation tasks, such as scene parsing or enhancing human pose estimation. Moreover, integrating low-level features into the ATR framework could further refine parsing accuracy.

In conclusion, this paper advances the field of human parsing by introducing an active template and shape-driven parsing framework which dramatically enhances the precision of segmenting human images into semantically meaningful parts. The demonstrated applicability and performance on large datasets lay a strong foundation for future innovations in related computer vision applications.