InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography Prediction
<|reference_start|>InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography Prediction: We propose a novel unsupervised cross-modal homography estimation framework, based on interleaved modality transfer and self-supervised homography prediction, named InterNet. InterNet integrates modality transfer and self-supervised homography estimation, introducing an innovative interleaved optimization framework to alternately promote both components. The modality transfer gradually narrows the modality gaps, facilitating the self-supervised homography estimation to fully leverage the synthetic intra-modal data. The self-supervised homography estimation progressively achieves reliable predictions, thereby providing robust cross-modal supervision for the modality transfer. To further boost the estimation accuracy, we also formulate a fine-grained homography feature loss to improve the connection between two components. Furthermore, we employ a simple yet effective distillation training technique to reduce model parameters and improve cross-domain generalization ability while maintaining comparable performance. Experiments reveal that InterNet achieves the state-of-the-art (SOTA) performance among unsupervised methods, and even outperforms many supervised methods such as MHN and LocalTrans.<|reference_end|>
@article{yu2024internet:,
    title={InterNet: Unsupervised Cross-modal Homography Estimation Based on
  Interleaved Modality Transfer and Self-supervised Homography Prediction},
    author={Junchen Yu, Si-Yuan Cao, Runmin Zhang, Chenghao Zhang, Jianxin Hu, Zhu
  Yu, Beinan Yu, Hui-liang Shen},
    journal={arXiv preprint arXiv:2409.17993},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17993},
    primaryClass={cs.CV}
}