arXiv:2412.00122v1 Announce Type: new Abstract: Learning from feedback has been shown to enhance the alignment between text prompts and images in text-to-image diffusion models. However, due to the lack of focus in feedback content, especially regarding the object type and quantity, these techniques struggle to accurately match text and images when faced with specified prompts. To address this issue, we propose an efficient fine-turning method with specific reward objectives, including three stages. First, generated images from diffusion model are detected to obtain the object categories and quantities. Meanwhile, the confidence of category and quantity can be derived from the detection results and given prompts. Next, we define a novel matching score, based on above confidence, to measure text-image alignment. It can guide the model for feedback learning in the form of a reward function. Finally, we fine-tune the diffusion model by backpropagation the reward function gradients to generate semantically related images. Different from previous feedbacks that focus more on overall matching, we place more emphasis on the accuracy of entity categories and quantities. Besides, we construct a text-to-image dataset for studying the compositional generation, including 1.7 K pairs of text-image with diverse combinations of entities and quantities. Experimental results on this benchmark show that our model outperforms other SOTA methods in both alignment and fidelity. In addition, our model can also serve as a metric for evaluating text-image alignment in other models. All code and dataset are available at https://github.com/kingniu0329/Visions.
The article “Learning from Feedback to Enhance Text-to-Image Alignment” addresses the challenge of accurately matching text prompts with images in text-to-image diffusion models. While previous techniques have shown improvement in alignment, they struggle when faced with specified prompts due to the lack of focus in feedback content regarding object type and quantity. To overcome this issue, the authors propose an efficient fine-tuning method with specific reward objectives, consisting of three stages. First, generated images are detected to obtain object categories and quantities. Then, a novel matching score is defined based on the confidence derived from the detection results and given prompts, guiding the model for feedback learning as a reward function. Finally, the diffusion model is fine-tuned using backpropagation of the reward function gradients to generate semantically related images. The authors emphasize the accuracy of entity categories and quantities, unlike previous approaches that focus more on overall matching. They also introduce a text-to-image dataset for studying compositional generation. Experimental results demonstrate that their model outperforms other state-of-the-art methods in both alignment and fidelity. Additionally, their model can serve as a metric for evaluating text-image alignment in other models.
Enhancing Text-Image Alignment with Specific Reward Objectives
Learning from feedback has proven to be beneficial in improving text-to-image diffusion models. However, existing techniques face challenges when accurately matching text and images based on specified prompts. These challenges arise due to the lack of focus in feedback content, particularly regarding object types and quantities.
To address this issue, we propose an efficient fine-tuning method that incorporates specific reward objectives. The method consists of three stages:
Stage 1: Object Detection and Confidence Estimation
In the first stage, we utilize object detection techniques to identify the object categories and quantities in the generated images from the diffusion model. By comparing the detection results with the given prompts, we can derive the confidence levels of both the object categories and quantities.
Stage 2: Novel Matching Score
In the next stage, we introduce a novel matching score that is based on the confidence levels obtained in the previous stage. This matching score serves as a measure of text-image alignment and guides the model for feedback learning in the form of a reward function.
Stage 3: Fine-tuning with Backpropagation
Finally, we fine-tune the diffusion model by backpropagating the gradients of the reward function. This enables the model to generate semantically related images that better align with the given text prompts. Notably, our approach places more emphasis on the accuracy of entity categories and quantities, unlike previous feedbacks that primarily focus on overall matching.
In addition, we have constructed a text-to-image dataset specifically designed for studying compositional generation. The dataset consists of 1.7K pairs of text and image with diverse combinations of entities and quantities. Experimental results on this benchmark demonstrate that our proposed model outperforms other state-of-the-art methods in terms of both alignment and fidelity.
Furthermore, our model can serve as a valuable metric for evaluating text-image alignment in other models. By leveraging the specific reward objectives and fine-tuning approach, we provide a solution that addresses the challenges faced by current text-to-image diffusion models.
All code and dataset related to our proposed method are openly available at https://github.com/kingniu0329/Visions. We encourage researchers and practitioners to explore and utilize these resources to further advance the field of text-to-image alignment.
Image credit: Pexels.com
The paper arXiv:2412.00122v1 discusses the challenge of accurately matching text prompts with images in text-to-image diffusion models. While learning from feedback has shown promise in improving alignment between text and images, the lack of specificity in feedback content, particularly regarding object type and quantity, hinders the accuracy of matching.
To address this issue, the authors propose an efficient fine-tuning method with specific reward objectives, consisting of three stages. Firstly, the generated images from the diffusion model are analyzed to detect object categories and quantities. By comparing the detection results with the given prompts, the confidence of category and quantity can be determined.
Next, a novel matching score is introduced based on the obtained confidence values. This matching score serves as a reward function, guiding the model in its feedback learning process. Unlike previous approaches that primarily focus on overall matching, this proposed method places greater emphasis on the accuracy of entity categories and quantities.
Furthermore, the authors have constructed a text-to-image dataset specifically designed for studying compositional generation. This dataset includes 1.7 K pairs of text-image combinations with diverse entities and quantities. Experimental results on this benchmark demonstrate that the proposed model outperforms other state-of-the-art methods in terms of both alignment and fidelity.
Importantly, the authors highlight that their model can also serve as a metric for evaluating text-image alignment in other models, indicating its potential for broader applications beyond their specific approach.
In summary, this paper presents a novel fine-tuning method with specific reward objectives to improve text-to-image alignment. By focusing on the accuracy of entity categories and quantities, the proposed model achieves superior performance compared to existing methods. The availability of their code and dataset further enhances the reproducibility and potential impact of their work.
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