Discrete Latent Perspective Learning for Segmentation and Detection
This addresses the problem of inconsistent semantic interpretation from varying perspectives for computer vision applications, offering a novel method that reduces reliance on multi-view data collection.
The paper tackles the challenge of perspective-invariant learning in computer vision by proposing the Discrete Latent Perspective Learning (DLPL) framework, which uses single-view images to enhance network performance across diverse scenarios and tasks, achieving significant improvements in detection and segmentation.
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).