多视角融合的分层互蒸馏：从所有可能的视角组合中学习
Hierarchical mutual distillation for multi-view fusion: Learning from all possible view combinations
AI总结：
本文提出一种新颖的多视角不确定性加权互蒸馏方法，通过分层互蒸馏提升预测一致性，有效利用各视角信息并缓解不确定预测的影响。
摘要：
多视角学习常面临有效利用不同角度和位置拍摄图像的挑战，尤其是在处理视角间不一致性和不确定性时更为突出。本文提出了一种新颖的多视角不确定性加权互蒸馏（MV-UWMD）方法。我们的方法通过在所有可能的视角组合中进行分层互蒸馏来增强预测一致性，包括单视角、部分多视角和全多视角预测。这引入了一种基于不确定性的加权机制，通过互蒸馏有效利用每个视角的独特信息，同时减轻不确定预测的影响。我们扩展了CNN-Transformer混合架构以促进在多个视角组合中的稳健特征学习和整合。我们使用了一个大规模、非结构化的数据集进行广泛实验，该数据集来自多样且非固定视角的拍摄。结果表明，MV-UWMD相比现有多视角学习方法在预测准确性和一致性方面有所提升。
英文摘要：
Multi-view learning often struggles to effectively leverage images captured from diverse angles and locations. Learning methods for unstructured multi-view images remain largely underexplored. We propose a novel Hierarchical Mutual Distillation for Multi-View Fusion (HMDMV) method, which can handle both structured and unstructured multi-view scenarios. It makes predictions utilizing all possible view combinations: single view, partial multi-view, and full multi-view. The method generates predictions for each view combination and then applies hierarchical mutual distillation to enhance inter-view consistency. An uncertainty-based weighting mechanism further refines the fusion process by adjusting the influence of each view combination according to its prediction confidence, reducing the impact of low-confidence views. Extensive experiments on large-scale structured and unstructured datasets demonstrate that HMDMV consistently achieves state-of-the-art classification accuracy. Another unique advantage of HMDMV is that it provides improved flexibility in inference, allowing for more or fewer view counts in inference than those used in training without additional processing. We also provide a light version with reduced training cost by designing an efficient strategy that randomly samples subsets of view combinations during each training iteration. These results highlight HMDMV's robustness in real-world settings where view availability is variable or incomplete. The code is available at https://github.com/labhai/HMDMV.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
