A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years. Existing approaches based on transformers mainly focus on exploring the affinity matrix to boost CAMs with global relationships. While in this work, we first perform a scrupulous examination towards the impact of successive affinity matrices and discover that they possess an inclination toward sparsification as the network approaches convergence, hence disclosing a manifestation of over-smoothing. Besides, it has been observed that enhanced attention maps tend to evince a substantial amount of extraneous background noise in deeper layers. Drawing upon this, we posit a daring conjecture that the undisciplined over-smoothing phenomenon introduces a noteworthy quantity of semantically irrelevant background noise, causing performance degradation. To alleviate this issue, we propose a novel perspective that highlights the objects of interest by investigating the regions of the trait, thereby fostering an extensive comprehension of the successive affinity matrix. Consequently, we suggest an adaptive re-activation mechanism (AReAM) that alleviates the issue of incomplete attention within the object and the unbounded background noise. AReAM accomplishes this by supervising high-level attention with shallow affinity matrices, yielding promising results. Exhaustive experiments conducted on the commonly used dataset manifest that segmentation results can be greatly improved through our proposed AReAM, which imposes restrictions on each affinity matrix in deep layers to make it attentive to semantic regions.

翻译：近年来，弱监督语义分割因其显著的高效性而备受关注。现有基于Transformer的方法主要致力于探索亲和矩阵以通过全局关系提升类激活图（CAMs）。然而，在本研究中，我们首次对连续亲和矩阵的影响进行了严谨分析，发现网络接近收敛时这些矩阵倾向于稀疏化，从而揭示了过平滑现象的一个表现。此外，观察发现增强后的注意力图在深层中往往包含大量无关背景噪声。基于此，我们提出一个大胆猜想：无约束的过平滑现象引入了大量语义无关的背景噪声，导致性能下降。为解决该问题，我们提出一个新颖视角，通过研究特征区域来突出目标对象，从而促进对连续亲和矩阵的深入理解。据此，我们提出自适应再激活机制（AReAM），该机制通过利用浅层亲和矩阵监督高层注意力，缓解了目标内部注意力不完整与无界背景噪声的问题。在常用数据集上的详尽实验表明，通过我们对深层每个亲和矩阵施加约束以使其关注语义区域的AReAM方法，分割结果可获得显著提升。