Deep spectral methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention compared to other tasks within the context of deep spectral methods. This paper addresses the fact that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. In fact, Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules: Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A new similarity metric called Bray-Curtis over Chebyshev (BoC) is proposed to address this issue. It takes into account the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS2019 dataset highlight the improvements achieved by the proposed channel reduction methods and the use of BoC instead of the conventional dot product for creating the affinity matrix. These improvements are observed in terms of mean Intersection over Union and extracted instance segments, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS

通过自监督学习提取特征并利用亲和矩阵的拉普拉斯特性进行特征分割的深度光谱方法在实例分割中受到的关注较少。本文提出了两个通道减少模块(NCR和DCR)，通过保留较低熵的通道并对具有较低标准差的通道进行修剪，以克服一些通道对实例分割任务准确性的干扰。此外，为了解决深度光谱方法常用的点积在实例分割中的敏感性问题，提出了一种新的相似度度量方法BoC，它考虑了特征的分布和值，提供了更强大的实例分割相似度度量。Youtube-VIS2019数据集上的定量和定性结果显示了提出的通道减少方法和使用BoC替代传统点积构建亲和矩阵的改进效果，表现为更好的平均交集联合和提取的实例分割图像。

深度光谱改进的无监督图像实例分割