review.txt

{
    "Summary": "The paper presents SepFormer, a novel transformer-based model for spectral domain image classification, demonstrating promising results on various datasets.",
    "Strengths": [
        "Introduces a new architecture (SepFormer) tailored to spectral image processing.",
        "Shows competitive performance on multiple datasets."
    ],
    "Weaknesses": [
        "Limited analysis on generalization capabilities.",
        "Potential limitations in real-world scenarios are not thoroughly addressed."
    ],
    "Originality": 4,
    "Quality": 3,
    "Clarity": 4,
    "Significance": 4,
    "Questions": [
        "How does SepFormer perform on unseen or real-world spectral data?",
        "What are the potential limitations and how can they be mitigated?"
    ],
    "Limitations": "The paper should provide more analysis on generalization capabilities and potential limitations in real-world scenarios.",
    "Ethical Concerns": false,
    "Soundness": 3,
    "Presentation": 4,
    "Contribution": 4,
    "Overall": 19,
    "Confidence": 4,
    "Decision": "accept with conditions"
}