Exploring Non-Autoregressive Image Captioning: Patterns and Semantics
Authors: Zihua Wang, Xu Yang, Yaya Shi, Haiyang Xu, Ming Yan, Ji Zhang, Fei Huang, Yu Zhang


Reviewer paper summaries: The paper designs a non-autoregressive image captioning method by combining sentence structure patterns and sentence semantics. The performance of the model is enhanced through various approaches, including knowledge distillation, efficient label selection, image pre-fusion at the decoding end, and a combined autoregressive and non-autoregressive augmentation method. Experimental evaluation was conducted on the MSCOCO public dataset.
Summary of strengths: - The overall writing is clear and easy to understand.
- Alalaition experiments are conduted on mscoco benchmark dataset with extensive results.
Summary of weaknesses: 1. The novelity of the proposed method is limited and does not effectively integrate sentence structure and semantics as claimed.
2. In the pre-fusion part of the model structure diagram, the vocabulary embedding is labeled as \(V_E\) and the image encoder features as \(V_T\), but the description in the formulas is exactly the opposite. 
3. The explanation of the ablation experiments is insufficient. For example, in the AR+NAR enhancement, N2 is not explained, yet N2 performs well. 
4. It is difficult to attribute the improvement in experimental performance to the design of the model; it is more likely due to the combination of methods such as knowledge distillation.
Comments, suggestions and typos: L167, a --> an.
Others see weakness.
Overall assessment scores: 2