Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks (2303.17602v1)

Abstract: Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent. We call this method SOLIDER, a Semantic cOntrollable seLf-supervIseD lEaRning framework. Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation. Meanwhile, we note that different downstream tasks always require different ratios of semantic information and appearance information. For example, human parsing requires more semantic information, while person re-identification needs more appearance information for identification purpose. So a single learned representation cannot fit for all requirements. To solve this problem, SOLIDER introduces a conditional network with a semantic controller. After the model is trained, users can send values to the controller to produce representations with different ratios of semantic information, which can fit different needs of downstream tasks. Finally, SOLIDER is verified on six downstream human-centric visual tasks. It outperforms state of the arts and builds new baselines for these tasks. The code is released in https://github.com/tinyvision/SOLIDER.

• The paper introduces SOLIDER, which enriches human representations by incorporating pseudo semantic labels in self-supervised learning.
• It features a conditional network with a semantic controller to dynamically balance semantic and appearance features for diverse tasks.
• Extensive tests on six tasks show that SOLIDER consistently outperforms benchmarks, setting a new standard for human-centric visual analysis.

Semantic Controllable Self-Supervised Learning for Human-Centric Visual Tasks

The paper presents a novel approach to enhancing self-supervised learning (SSL) for human-centric visual tasks. The proposed framework, named SOLIDER (Semantic cOntrollable seLf-supervIseD lEaRning framework), introduces semantic controllability into SSL, addressing limitations in traditional methods which predominantly rely on visual appearance features.

Key Contributions

The primary objective of SOLIDER is to produce a general human representation from unlabeled data that can be customized for a wide range of downstream tasks. This is achieved through a series of innovative techniques:

1. Semantic Label Utilization: The framework uses prior knowledge from human images to establish pseudo semantic labels, enriching the learned representation with semantic information. The authors demonstrate that this is crucial for tasks needing nuanced understanding beyond mere appearance.
2. Conditional Representation Adjustments: Recognizing that different tasks require varying balances of semantic and appearance information, SOLIDER incorporates a conditional network with a semantic controller. This controller enables dynamic adjustments of the representation according to task-specific requirements.
3. Verification on Multiple Tasks: SOLIDER is tested extensively across six human-centric tasks, including person re-identification, attribute recognition, and pose estimation, consistently outperforming existing benchmarks.

Methodological Insights

One of SOLIDER’s core innovations is the use of human-centric prior knowledge to address limitations found in traditional SSL methods like DINO. By generating semantically enriched pseudo labels and applying these to cluster features at the token level, SOLIDER effectively introduces greater semantic depth into the learned representations.

Furthermore, the introduction of a semantic controller enables post-training adaptation of learned representations via a controllable parameter, denoted as λ. This enables the modulation of semantic information and appearance features in the derived representations, supporting diverse task requirements.

Experimental Analysis

The framework is validated through rigorous testing and achieves notable improvements across various tasks. For instance, in person search and pedestrian detection, tasks that can benefit significantly from semantic understanding, SOLIDER’s semantic focus yields enhanced performance. These empirical results substantiate the framework's robustness and adaptability, demonstrating that SOLIDER can serve as a superior pre-training model within human-centric visual domains.

Theoretical and Practical Implications

The SOLIDER framework represents a significant step towards more versatile and task-attuned self-supervised representations. By addressing the balance between semantic and appearance features dynamically, this work provides a foundation for future work in semantic control within SSL paradigms. The integration of a controllable network component highlights potential for further exploration in adaptable model architectures within AI.

Future Research Directions

Areas for future exploration include extending the semantic control mechanism to other domains beyond human-centric tasks, potentially generalizing this approach to varying datasets and SSL forms. Further optimization could enhance the computational efficiency of semantic clustering and control processes, broadening applicability to more resource-constrained settings.

In conclusion, the SOLIDER framework provides a refined approach to human-centric image analysis, setting a new standard in self-supervised learning applications. Its design not only enhances current methodologies but also opens pathways for enriched semantic interpretation in machine learning tasks.