Leveraging Pose-Consistent Generated Images for Effective Self-Supervised Representation Learning

핵심 개념

By generating pose-consistent, appearance-varying images and employing pose-consistent multi-positive contrastive learning, the proposed GenPoCCL method effectively captures the structural features of the human body, outperforming existing methods in various human-centric perception tasks even with significantly less training data.

초록

The paper presents a novel method called GenPoCCL (Generated image leveraged Pose Consistent Contrastive Learning) for self-supervised pre-training on human-centric perception tasks.
Key highlights:

Leverages the ability to generate visually distinct images with identical human poses to create pose-consistent, appearance-varying training data.
Introduces a multi-positive contrastive learning approach that aligns features of images with the same human pose, enabling the model to effectively learn structural features of the human body.
Proposes a [POSE] token in addition to the [CLS] token to better capture both discriminative human features and human-pose related features.
Experimentally demonstrates that GenPoCCL outperforms existing methods in a variety of human-centric perception tasks, including 2D pose estimation, person re-identification, and pedestrian attribute recognition, while using less than 1% of the training data volume.
Discusses limitations such as suboptimal quality of generated images, especially for human faces, and potential improvements through employing large language models for caption generation.

통계

GenPoCCL trained on the GenCOCO dataset, which consists of 117,126 unique human poses with 5 variations each, outperforms StableRep trained on the 83M-sample GenCC12M dataset in 2D pose estimation on MPII (+0.9%) and MSCOCO (+0.1%).
GenPoCCL trained on the GenCOCO dataset outperforms StableRep trained on the 83M-sample GenCC12M dataset in person re-identification on Market-1501 by +5.5% mAP.
GenPoCCL trained on the GenCOCO dataset outperforms StableRep trained on the 83M-sample GenCC12M dataset in text-to-image person re-identification on RSTPReid by +3.2% Rank-1.
GenPoCCL trained on the GenCOCO dataset outperforms StableRep trained on the 83M-sample GenCC12M dataset in pedestrian attribute recognition on PA-100K by +0.6% mA.

인용구

"By treating images generated from the same human body pose condition as positive pairs as shown in Fig. 1, we propose pose-consistent multi-positive contrastive learning to guide the model with human body pose constraints."
"Remarkably, it achieves superior performance to current methods with under 1% of the generative data previously needed."

핵심 통찰 요약

Multi Positive Contrastive Learning with Pose-Consistent Generated Images

by Sho Inayoshi... 게시일 arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03256.pdf

더 깊은 질문

How can the quality of generated images, especially for human faces, be further improved to enhance the performance of GenPoCCL in human-centric perception tasks?

To improve the quality of generated images, particularly for human faces, several strategies can be implemented:

High-Resolution Training: Training the generative model on higher resolution images can lead to more detailed and realistic outputs, especially for facial features that require fine details.

Diverse Dataset: Utilizing a diverse dataset with a wide range of facial features, skin tones, and expressions can help the model learn a more comprehensive representation of human faces, leading to better image generation.

Fine-Tuning: Fine-tuning the generative model specifically for facial features can help it focus on capturing the nuances of human faces, such as expressions, emotions, and details like wrinkles or facial hair.

Attention Mechanisms: Implementing attention mechanisms in the generative model can help it focus on specific regions of the image, such as the face, improving the quality of facial features in the generated images.

Data Augmentation: Applying data augmentation techniques specifically tailored for facial images, such as rotation, scaling, and color manipulation, can help the model learn variations in facial appearances.

By incorporating these strategies, the quality of generated images, especially for human faces, can be enhanced, leading to improved performance in human-centric perception tasks.

How can the proposed GenPoCCL framework be extended to other domains beyond human-centric perception, where consistent and diverse synthetic data could benefit representation learning?

The GenPoCCL framework can be extended to other domains beyond human-centric perception by leveraging different types of conditioning information to generate diverse and informative synthetic training data for representation learning. Some ways to extend the framework include:

Object Recognition: Utilizing object attributes, such as shape, color, and texture, as conditioning information to generate diverse synthetic data for training models in object recognition tasks.

Scene Understanding: Incorporating scene attributes like lighting conditions, weather, and time of day to generate synthetic data for training models in scene understanding tasks.

Medical Imaging: Using patient-specific information, such as medical history, demographics, and symptoms, to generate diverse synthetic medical images for training models in medical imaging tasks.

Autonomous Driving: Leveraging environmental factors like road conditions, traffic density, and weather patterns to generate synthetic data for training models in autonomous driving tasks.

By adapting the GenPoCCL framework to different domains and incorporating relevant conditioning information, it can be applied to a wide range of tasks where consistent and diverse synthetic data can benefit representation learning.

What other types of conditioning information, beyond human poses, could be leveraged to generate more diverse and informative synthetic training data for self-supervised representation learning?

Beyond human poses, several other types of conditioning information can be leveraged to generate diverse and informative synthetic training data for self-supervised representation learning. Some examples include:

Facial Expressions: Using facial expression labels to generate images with varying emotions, expressions, and moods, which can enhance the model's understanding of human emotions.

Environmental Factors: Incorporating environmental factors like background scenery, lighting conditions, and weather patterns to generate images that reflect different environmental contexts.

Object Attributes: Utilizing object attributes such as size, shape, color, and texture to generate images with diverse objects and variations in object properties.

Temporal Information: Including temporal information like motion, time of day, and sequence of events to generate dynamic images that capture changes over time.

Contextual Cues: Leveraging contextual cues such as relationships between objects, spatial arrangements, and interactions to generate images that convey rich contextual information.

By incorporating a diverse range of conditioning information beyond human poses, the synthetic training data can be enriched with various attributes and characteristics, leading to more robust and comprehensive representation learning.