重新思考目标显著性排序：一种新颖的全流程处理范式
Rethinking Object Saliency Ranking: A Novel Whole-flow Processing Paradigm
AI总结：
针对显著性排序中GT顺序不合理、多任务冲突及回归方法复杂等问题，本文提出覆盖GT生成、网络设计与训练协议的全流程统一框架，并在SALICON上超越现有方法。
摘要：
现有的显著性目标检测方法能够预测突出视觉显著区域的二值图。然而，这些方法在区分多个目标的相对重要性及其相互关系方面能力有限，这可能在依赖多个目标相对重要性的下游任务中导致错误并降低准确性。为克服这一问题，本文提出了一种显著性排序的新范式，旨在完全聚焦于按照显著目标的“重要性顺序”对其进行排序。尽管先前工作已展现出良好性能，但它们仍面临不适定问题。首先，显著性排序真值（GT）顺序生成方法并不合理，因为确定正确的排序顺序缺乏明确定义，从而导致误报。其次，训练排序模型仍然具有挑战性，因为大多数显著性排序方法遵循多任务范式，造成不同任务之间的冲突与权衡。第三，现有基于回归的显著性排序方法由于依赖基于实例掩码的显著性排序顺序，对显著性排序模型而言较为复杂。这些方法需要大量数据才能准确执行，并且可能难以有效实现。为解决这些问题，本文深入分析了其成因，并从“GT数据生成”“网络结构设计”和“训练协议”的角度提出了显著性排序任务的全流程处理范式。大量实验以及公平、合理的比较表明，所提方法在广泛使用的SALICON数据集上优于现有最先进方法。显著性排序任务仍处于起步阶段，我们提出的统一框架可作为指导未来工作的基础策略。
英文摘要：
Existing salient object detection methods are capable of predicting binary maps that highlight visually salient regions. However, these methods are limited in their ability to differentiate the relative importance of multiple objects and the relationships among them, which can lead to errors and reduced accuracy in downstream tasks that depend on the relative importance of multiple objects. To conquer, this paper proposes a new paradigm for saliency ranking, which aims to completely focus on ranking salient objects by their "importance order". While previous works have shown promising performance, they still face ill-posed problems. First, the saliency ranking ground truth (GT) orders generation methods are unreasonable since determining the correct ranking order is not well-defined, resulting in false alarms. Second, training a ranking model remains challenging because most saliency ranking methods follow the multi-task paradigm, leading to conflicts and trade-offs among different tasks. Third, existing regression-based saliency ranking methods are complex for saliency ranking models due to their reliance on instance mask-based saliency ranking orders. These methods require a significant amount of data to perform accurately and can be challenging to implement effectively. To solve these problems, this paper conducts an in-depth analysis of the causes and proposes a whole-flow processing paradigm of saliency ranking task from the perspective of "GT data generation", "network structure design" and "training protocol". The proposed approach outperforms existing state-of-the-art methods on the widely-used SALICON set, as demonstrated by extensive experiments with fair and reasonable comparisons. The saliency ranking task is still in its infancy, and our proposed unified framework can serve as a fundamental strategy to guide future work.
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。
