He Lian

Xiangwen Shi, Zhe Cui, Shaobing Zhang et al.

Few-shot semantic segmentation aims at recognizing the object regions of unseen categories with only a few annotated examples as supervision. The key to few-shot segmentation is to establish a robust semantic relationship between the support and query images and to prevent overfitting. In this paper, we propose an effective Multi-similarity Hyperrelation Network (MSHNet) to tackle the few-shot semantic segmentation problem. In MSHNet, we propose a new Generative Prototype Similarity (GPS), which together with cosine similarity can establish a strong semantic relation between the support and query images. The locally generated prototype similarity based on global feature is logically complementary to the global cosine similarity based on local feature, and the relationship between the query image and the supported image can be expressed more comprehensively by using the two similarities simultaneously. In addition, we propose a Symmetric Merging Block (SMB) in MSHNet to efficiently merge multi-layer, multi-shot and multi-similarity hyperrelational features. MSHNet is built on the basis of similarity rather than specific category features, which can achieve more general unity and effectively reduce overfitting. On two benchmark semantic segmentation datasets Pascal-5i and COCO-20i, MSHNet achieves new state-of-the-art performances on 1-shot and 5-shot semantic segmentation tasks.

Lisong He, Yi Yang, Xiangyu Chang

As the right to be forgotten becomes legislated worldwide, machine unlearning mechanisms have emerged to efficiently update models for data deletion and enhance user privacy protection. However, existing machine unlearning algorithms frequently neglect the fact that different data points may contribute unequally to model performance (i.e., heterogeneous data values). Treat them equally in machine unlearning procedure can potentially degrading the performance of updated models. To address this limitation, we propose Data Value-Weighted Unlearning (DVWU), a general unlearning framework that accounts for data value heterogeneity into the unlearning process. Specifically, we design a weighting strategy based on data values, which are then integrated into the unlearning procedure to enable differentiated unlearning for data points with varying utility to the model. The DVWU framework can be broadly adapted to various existing machine unlearning methods. We use the one-step Newton update as an example for implementation, developing both output and objective perturbation algorithms to achieve certified unlearning. Experiments on both synthetic and real-world datasets demonstrate that our methods achieve superior predictive performance and robustness compared to conventional unlearning approaches. We further show the extensibility of our framework on gradient ascent method by incorporating the proposed weighting strategy into the gradient terms, highlighting the adaptability of DVWU for broader gradient-based deep unlearning methods.