Advancing Model Training with Generated Images

Model pre-training has become very important in many tasks that recognize different objects or people. Recently, image generation technology has grown quickly, allowing us to create many training images. This has led to new ways to train models using these Generated Images. However, while generated images work well for basic tasks like classifying images, they struggle with more complex tasks such as understanding human body Poses.

In this article, we present a new method called GenPoCCL, which stands for Generated image leveraged Pose Consistent Contrastive Learning. We focus on creating different images that show the same human pose and then train our model to learn about the structure of the human body using these images. Our method does a great job at capturing important features of human body structures, even when we use fewer training images than typical methods.

#Image Generation

Generating images has always been an important part of training models. Usually, a large amount of data is needed, which can be hard to collect. However, new generative models like GANs and diffusion models have dramatically improved the quality of generated images. Diffusion models, in particular, have become popular because they create detailed and realistic images.

These generative models allow us to create images based on certain conditions. For example, we can give them guidelines about the pose of a human and receive images back that match these conditions. However, using only text to direct image generation can limit how effectively we can capture what we want the image to look like.

To address this issue, recent technologies have made it possible to have better control over the images being generated. By adding extra features as guidance, we can create images of people in specific poses. In our work, we take advantage of this controllable generation to create images that share the same pose but look different. This helps us generate more data for training our models.

#Representation Learning

Representation learning allows a model to learn useful features from data on its own. This means that instead of needing a lot of manual work to label data, the model can learn important patterns by itself. In recent years, two main approaches have been popular in representation learning: masked image modeling and contrastive learning.

Masked image modeling focuses on reconstructing parts of an image that have been hidden, while contrastive learning teaches the model to differentiate between similar and different pairs of images. A good model in contrastive learning will learn better when it has good examples to compare.

Recent studies have shown that creating pairs of images that are similar but have small differences can help improve the learning process. Some existing methods create these pairs from images generated from a main prompt, but there is still room for better learning, especially for tasks that focus on human characteristics.

#The Proposed Method

Our research proposes a method that generates images of people in the same pose but with different appearances. By treating these generated images as pairs, we can guide the model to learn better about human body structures. The special token we introduced, called the [POSE] token, helps the model to learn features related to human poses.

The goal of our GenPoCCL method is to align images with similar poses in a way that makes it easier for the model to understand the human body. Using our approach, we can improve the model's performance by allowing it to focus on important structural features, independent of how the background looks or the specific appearance of the person.

#Overview of the Pipeline

In our pipeline, we first create images from a human pose label and use these for training. The images generated share common characteristics but appear different due to variations introduced during generation. We then apply specific masks to these images and process them through a model that extracts features. The features of images with the same human pose are aligned using the [CLS] token, while the [POSE] token helps to guide the model in understanding the human structure better.

We have found that using generated images works well for pre-training purposes. The generated images allow us to provide enough data for the model to learn effectively without needing to rely on real images, which can be limited in variety.

#Creating Generated Datasets

To test our method, we created two generated datasets: the GenCOCO dataset and the GenLUPerson dataset. The GenCOCO dataset consists of various human poses with multiple appearance variations. We ensure that we capture enough diversity in our dataset by excluding images that do not meet certain quality criteria. The second dataset, GenLUPerson, is much larger, containing many human poses with three variations each.

This careful generation of datasets is crucial because it allows us to have the right images for the model to learn from. Both datasets help illustrate how our method can produce high-quality training data for different tasks that involve understanding human characteristics.

#Learning with Contrastive Methods

The learning approach we adopted focuses on improving how the model understands human structures. By using multi-positive contrastive learning, we can effectively bring images of the same pose closer together in the feature space. This helps the model capture meaningful features that are less affected by the appearance of the images or the background details.

We enhance our approach with the [POSE] token, which allows for better alignment of features related to human poses. The result is a model that learns to identify and understand the unique characteristics of human bodies more effectively. This is especially important when working on tasks that require a good understanding of human attributes.

#Training Details

For training, we use the two datasets we created. We set up a large enough input resolution to capture important details while using a specific batch size to manage computation needs. Different settings are applied based on the dataset to ensure that the model learns effectively.

To validate our method, we performed several human-centric perception tasks, including 2D human pose estimation, person re-identification, and pedestrian attribute recognition. Each task has its own set of performance metrics, which are important for evaluating how well our GenPoCCL method works compared to existing methods.

#Main Results

We compare our GenPoCCL method against other pre-training methods that also use generated images. The results show that our approach can outperform others even when we use significantly fewer training samples. For example, GenPoCCL achieved better performance on human-centric perception tasks than the widely used StableRep method, demonstrating the effectiveness of using our approach.

Interestingly, while some specific tasks did not see increased performance, this could be attributed to other factors in the datasets and tasks. Overall, the results indicate that using pose-consistent generated images can lead to better learning outcomes in real-world applications.

#Ablation Analysis

To ensure the effectiveness of our proposed components, we conducted ablation studies. This means we tested our model in a variety of configurations to see how each part contributed to overall performance. By looking at different combinations, we determined that using the [POSE] token significantly enhances performance in tasks that focus on human features.

Other settings confirmed that when we allowed the model to focus on both the discriminative features and pose alignment, we achieved better results. This insight into the significance of each part of our method is valuable for further refining our approach in future research.

#Limitations

While our method has proven effective, it also comes with limitations. The quality of generated images can sometimes be lower than ideal, especially regarding human faces. We also acknowledge some challenges with pose consistency and the way generated data relates to actual conditions.

Moreover, the dataset we built for GenLUPerson relies on a rule-based caption generation method, which may not capture all the nuances we would want. Future research could explore better caption generation methods to enhance the quality of our datasets.

#Conclusion

In summary, our research presents the GenPoCCL approach to training models using generated images for human-centric perception tasks. By creating pose-consistent images with diverse appearances and using multi-positive contrastive learning, we are capable of capturing the structural features of the human body effectively, even with less generated data than is typically required.

The results highlight how our method outperforms existing techniques across various tasks, showing promise for future developments in this field. By continuing to refine our techniques and exploring new methods, we can further improve how we teach models to understand human attributes and characteristics.