Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing (1703.05446v2)

Abstract: Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark "Look into Person (LIP)" that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analyses of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.

• The paper introduces a large-scale LIP dataset with over 50,000 annotated images that surpass existing benchmarks in scope and detail.
• The paper proposes a self-supervised structure-sensitive learning framework that leverages inherent human pose cues to enhance parsing accuracy.
• Comparative evaluations with state-of-the-art models demonstrate improved handling of occlusions and complex human poses.

An Essay on "Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing"

The paper "Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing" by Gong et al. contributes to the field of computer vision with a focus on the challenging task of human parsing. The authors introduce a new large-scale dataset, "Look into Person (LIP)," which significantly enhances the variability and complexity faced in human parsing by providing over 50,000 images annotated with 19 semantic part labels. This dataset surpasses existing datasets in terms of scale, diversity, and annotation detail, offering a robust benchmark for subsequent research efforts.

Dataset and Benchmark

The LIP dataset is a notable advancement in the field of human-centric image analysis. It contains a vast array of human images captured under diverse conditions, challenging existing models with occlusions, diverse viewpoints, and complex backgrounds. The dataset's comprehensive annotation facilitates detailed scrutiny of parsing methods, offering insights into the strengths and limitations of current approaches.

The paper conducts a comparative analysis of state-of-the-art methods like SegNet, FCN-8s, DeepLabV2, and Attention models on the LIP dataset. The evaluation reveals the increased difficulty of human parsing tasks compared to traditional object segmentation, underlining the intricacies involved in distinguishing finely detailed human parts.

Self-supervised Structure-sensitive Learning

A key contribution of this research is the introduction of a self-supervised structure-sensitive learning framework, designed to enhance human parsing accuracy by integrating implicit human pose structures. This approach distinguishes itself by generating joint configurations directly from parsing annotations without requiring extra supervision. By employing a novel structure-sensitive loss, the framework ensures semantic consistency in predicted parsing outcomes, addressing common parsing challenges such as occlusions and ambiguous spatial layouts.

The proposed method outperforms existing models, particularly under scenarios with occlusions and complex poses. By leveraging human joint structures, the framework achieves improved accuracy in distinguishing spatially confusing regions and smaller object parts.

Implications and Future Directions

The introduction of the LIP dataset and the self-supervised learning framework has significant implications for both theoretical advancements and practical applications. The dataset sets a new standard for human parsing challenges, encouraging the development of more sophisticated models. Self-supervised structure-sensitive learning provides a pathway for incorporating high-level human pose understanding into pixel-wise parsing tasks, potentially influencing future methods in human-centric analysis and beyond.

Future research may build upon this work by exploring model designs that further integrate structure-sensitive learning in different domains. There is also potential for extending these approaches to multi-modal datasets, allowing a richer contextual understanding of human activities.

Conclusion

This paper delivers a substantial dataset and an innovative learning framework, marking notable progress in human parsing. The insights drawn from the LIP dataset's evaluations provide a solid groundwork for future exploration in fine-grained image analysis and the development of more nuanced computational models. The self-supervised structure-sensitive approach is a promising direction, showcasing how intrinsic human structure can be harnessed to improve computational parsing tasks.