ESAN: An Efficient Semantic Attention Network for Remote Sensing Image Change Captioning
Authors: Qiaoli Sun, Yan Wang, Xiaoyu Song, HongyiDOng


Reviewer paper summaries: This paper introduces a model to address the challenge of accurately describing changes in remote sensing images with high-quality text. They proposed ESAN, incorporates a Global Efficient Semantic Representation (GESR) module on the feature map to understand complex scenes, a Cross-Semantic Feature Enhancement (CSFE) module to distinguish meaningful changes, and an adaptive multi-layer Transformer decoder to generate high-quality change descriptions. Contributions are:

1. Authors design the GESR module to enhance feature extraction of global semantics in remote sensing images, and introduce the CSFE module for accurate identification and description of fine-grained changes.
2. The performance of ESAN in Dubai-CC dataset is good.
3. The qualitative evaluation section of the paper is particularly commendable for its clarity, showcasing where the relavent change areas or invariant objects that the model can detect and describe.
Summary of strengths: 1. The paper presents a approach to remote sensing image change captioning by using a semantic attention network that can outperform some existing methods.
   
2. The ablation study and qualitative evaluation is good. The heatmap demonstration is particularly intuitive and aids in understanding the model's performance.

3. The introduction of the ESAN model is clear and well-structured, presenting the concepts in a formalized and accessible manner.
Summary of weaknesses: 1. The main structure of ESAN is just non-local blocks for image inputs (https://arxiv.org/pdf/1711.07971). A little changed were made to apply in Change Captioning domain.

2. Despite authors applied non-local structure into Change Captioning, the results, while improved over some methods, may not be as groundbreaking or impressive as expected. Recent approaches such as Chg2Cap and Prompt-CC in LEVIR-CC is not worse than ESAN. The model is evaluated on two representative datasets, experiments on more datasets may help in validating its effectiveness across different source of data.

3. Region-based associations between change captions and image contexts can be further studied (referring expression like associations). Can model handle complex relationships between different parts of visiual context and caption?
Comments, suggestions and typos: N/A
Overall assessment scores: 2.5