Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline
<|reference_start|>Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile Baseline: Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degrades in classifying in-the-wild images. Furthermore, we observed that in-the-wild plant images may exhibit similar appearances across various diseases (i.e., small inter-class discrepancy) while the same diseases may look quite different (i.e., large intra-class variance). Motivated by this observation, we propose an in-the-wild multimodal plant disease recognition dataset that contains the largest number of disease classes but also text-based descriptions for each disease. Particularly, the newly provided text descriptions are introduced to provide rich information in textual modality and facilitate in-the-wild disease classification with small inter-class discrepancy and large intra-class variance issues. Therefore, our proposed dataset can be regarded as an ideal testbed for evaluating disease recognition methods in the real world. In addition, we further present a strong yet versatile baseline that models text descriptions and visual data through multiple prototypes for a given class. By fusing the contributions of multimodal prototypes in classification, our baseline can effectively address the small inter-class discrepancy and large intra-class variance issues. Remarkably, our baseline model can not only classify diseases but also recognize diseases in few-shot or training-free scenarios. Extensive benchmarking results demonstrate that our proposed in-the-wild multimodal dataset sets many new challenges to the plant disease recognition task and there is a large space to improve for future works.<|reference_end|>
@article{wei2024benchmarking,
    title={Benchmarking In-the-wild Multimodal Disease Recognition and A Versatile
  Baseline},
    author={Tianqi Wei, Zhi Chen, Zi Huang, Xin Yu},
    journal={arXiv preprint arXiv:2408.03120},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03120},
    primaryClass={cs.CV}
}