Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition
<|reference_start|>Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition: Some visual recognition tasks are more challenging then the general ones as they require professional categories of images. The previous efforts, like fine-grained vision classification, primarily introduced models tailored to specific tasks, like identifying bird species or car brands with limited scalability and generalizability. This paper aims to design a scalable and explainable model to solve Professional Visual Recognition tasks from a generic standpoint. We introduce a biologically-inspired structure named Pro-NeXt and reveal that Pro-NeXt exhibits substantial generalizability across diverse professional fields such as fashion, medicine, and art-areas previously considered disparate. Our basic-sized Pro-NeXt-B surpasses all preceding task-specific models across 12 distinct datasets within 5 diverse domains. Furthermore, we find its good scaling property that scaling up Pro-NeXt in depth and width with increasing GFlops can consistently enhances its accuracy. Beyond scalability and adaptability, the intermediate features of Pro-NeXt achieve reliable object detection and segmentation performance without extra training, highlighting its solid explainability. We will release the code to foster further research in this area.<|reference_end|>
@article{wu2024not,
    title={Not just Birds and Cars: Generic, Scalable and Explainable Models for
  Professional Visual Recognition},
    author={Junde Wu and Jiayuan Zhu and Min Xu and Yueming Jin},
    journal={arXiv preprint arXiv:2403.05703},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05703},
    primaryClass={cs.CV}
}