再探图像抠图中的上下文聚合
Revisiting Context Aggregation for Image Matting
AI总结：
针对现有抠图方法的上下文聚合模块易受训练推理图像尺寸差异导致的尺度偏移影响、性能下降的问题，研究发现无额外聚合模块的基础编解码网络可学习更通用的上下文聚合，据此提出带混合Transformer主干与大图像训练策略的AEMatter，在5个数据集上性能大幅超越SOTA。
摘要：
传统研究强调了上下文信息在提升抠图性能中的重要性。因此，基于深度学习的抠图方法着力设计基于池化或亲和度的上下文聚合模块，以取得更优结果。然而，这类模块无法很好地应对训练与推理阶段因图像尺寸差异导致的上下文尺度偏移，进而造成抠图性能下降。本文中，我们重新审视了抠图网络的上下文聚合机制，发现不带任何上下文聚合模块的基础编解码网络实际上能学习到更具通用性的上下文聚合，从而取得比现有方法更优的抠图性能。基于这一发现，我们提出了AEMatter——一个结构简单却十分高效的抠图网络。AEMatter采用混合Transformer主干网络，搭配外观增强的轴向学习（AEAL）模块，构建出具备强上下文聚合学习能力的基础网络。此外，AEMatter利用大图像训练策略辅助网络从数据中学习上下文聚合。在5个主流抠图数据集上的大量实验表明，所提出的AEMatter大幅优于当前最先进的抠图方法。
英文摘要：
Traditional studies emphasize the significance of context information in improving matting performance. Consequently, deep learning-based matting methods delve into designing pooling or affinity-based context aggregation modules to achieve superior results. However, these modules cannot well handle the context scale shift caused by the difference in image size during training and inference, resulting in matting performance degradation. In this paper, we revisit the context aggregation mechanisms of matting networks and find that a basic encoder-decoder network without any context aggregation modules can actually learn more universal context aggregation, thereby achieving higher matting performance compared to existing methods. Building on this insight, we present AEMatter, a matting network that is straightforward yet very effective. AEMatter adopts a Hybrid-Transformer backbone with appearance-enhanced axis-wise learning (AEAL) blocks to build a basic network with strong context aggregation learning capability. Furthermore, AEMatter leverages a large image training strategy to assist the network in learning context aggregation from data. Extensive experiments on five popular matting datasets demonstrate that the proposed AEMatter outperforms state-of-the-art matting methods by a large margin.
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。
