Enhancing Intent Understanding for Ambiguous Prompt: A Human-Machine Co-Adaption Strategy
Authors: Yangfan He, Bai Yuxuan, Li Sun, TIANYU SHI


Reviewer paper summaries: This work proposes a human-machine co-adaptation strategy that maximizes the mutual information between user prompts and the modified pictures. This optimization goal is intended to enhance the system's ability to adapt to user needs.
Summary of strengths: They find that an improved model reduces the need for multiple adjustments. They claim to have collected multi-round dialogue datasets with prompt-image pairs and user intent, but it is unclear whether these will be available in the future.
Summary of weaknesses: This paper is not well-written. The figures appear to be drafted, and the structure lacks logical coherence. It seems more like an engineering project than an academic paper.
Comments, suggestions and typos: It seems odd to place Figure 1 and Figure 2 at the beginning, as they do not align well with the main idea of the paper.

Figure 3 appears to illustrate the main pipeline for the method, but it is poorly drafted, with an imbalanced proportion of images and text, and the images are quite rough. Most importantly, it is unclear how Figure 3 represents the human-machine co-adaptation, which is the main idea of the paper, making it difficult for readers to understand.

The paper is difficult to read, with excessive redundant descriptions of unrelated works, particularly in Section 3.

In the experiments, metrics for comparisons, such as "User Satisfaction" and "Aesthetic Score," should be clearly defined for better understanding.

The authors should maintain consistency in the use of concepts and terms throughout the entire paper.
Overall assessment scores: 1