Discrete Latent Perspective Learning for Segmentation and Detection
<|reference_start|>Discrete Latent Perspective Learning for Segmentation and Detection: In this paper, we address the challenge of Perspective-Invariant Learning in machine learning and computer vision, which involves enabling a network to understand images from varying perspectives to achieve consistent semantic interpretation. While standard approaches rely on the labor-intensive collection of multi-view images or limited data augmentation techniques, we propose a novel framework, Discrete Latent Perspective Learning (DLPL), for latent multi-perspective fusion learning using conventional single-view images. DLPL comprises three main modules: Perspective Discrete Decomposition (PDD), Perspective Homography Transformation (PHT), and Perspective Invariant Attention (PIA), which work together to discretize visual features, transform perspectives, and fuse multi-perspective semantic information, respectively. DLPL is a universal perspective learning framework applicable to a variety of scenarios and vision tasks. Extensive experiments demonstrate that DLPL significantly enhances the network's capacity to depict images across diverse scenarios (daily photos, UAV, auto-driving) and tasks (detection, segmentation).<|reference_end|>
@article{ji2024discrete,
    title={Discrete Latent Perspective Learning for Segmentation and Detection},
    author={Deyi Ji, Feng Zhao, Lanyun Zhu, Wenwei Jin, Hongtao Lu, Jieping Ye},
    journal={arXiv preprint arXiv:2406.10475},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10475},
    primaryClass={cs.CV}
}