Haichao Shi

Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust representations from various augmented views. However, existing GCL methods typically depend on consistent stochastic augmentations, which overlook their impact on the intrinsic structure of the spectral domain, thereby limiting the model's ability to generalize effectively. To address these limitations, we propose a novel paradigm called AS-GCL that incorporates asymmetric spectral augmentation for graph contrastive learning. A typical GCL framework consists of three key components: graph data augmentation, view encoding, and contrastive loss. Our method introduces significant enhancements to each of these components. Specifically, for data augmentation, we apply spectral-based augmentation to minimize spectral variations, strengthen structural invariance, and reduce noise. With respect to encoding, we employ parameter-sharing encoders with distinct diffusion operators to generate diverse, noise-resistant graph views. For contrastive loss, we introduce an upper-bound loss function that promotes generalization by maintaining a balanced distribution of intra- and inter-class distance. To our knowledge, we are the first to encode augmentation views of the spectral domain using asymmetric encoders. Extensive experiments on eight benchmark datasets across various node-level tasks demonstrate the advantages of the proposed method.

Image forgeries can entirely change the semantic information of an image, and can be used for unscrupulous purposes. In this paper, we propose a novel image forgery localization network named as MUN, which consists of an M^3 encoder and a UN decoder. Firstly, the M^3 encoder is constructed based on a Multi-scale Max-pooling query module to extract Multi-clue forged features. Noiseprint++ is adopted to assist the RGB clue, and its deployment methodology is discussed. A Multi-scale Max-pooling Query (MMQ) module is proposed to integrate RGB and noise features. Secondly, a novel UN decoder is proposed to extract hierarchical features from both top-down and bottom-up directions, reconstructing both high-level and low-level features at the same time. Thirdly, we formulate an IoU-recalibrated Dynamic Cross-Entropy (IoUDCE) loss to dynamically adjust the weights on forged regions according to IoU which can adaptively balance the influence of authentic and forged regions. Last but not least, we propose a data augmentation method, i.e., Deviation Noise Augmentation (DNA), which acquires accessible prior knowledge of RGB distribution to improve the generalization ability. Extensive experiments on publicly available datasets show that MUN outperforms the state-of-the-art works.

Contemporary deep face recognition techniques predominantly utilize the Softmax loss function, designed based on the similarities between sample features and class prototypes. These similarities can be categorized into four types: in-sample target similarity, in-sample non-target similarity, out-sample target similarity, and out-sample non-target similarity. When a sample feature from a specific class is designated as the anchor, the similarity between this sample and any class prototype is referred to as in-sample similarity. In contrast, the similarity between samples from other classes and any class prototype is known as out-sample similarity. The terms target and non-target indicate whether the sample and the class prototype used for similarity calculation belong to the same identity or not. The conventional Softmax loss function promotes higher in-sample target similarity than in-sample non-target similarity. However, it overlooks the relation between in-sample and out-sample similarity. In this paper, we propose Global Cross-Entropy loss (GCE), which promotes 1) greater in-sample target similarity over both the in-sample and out-sample non-target similarity, and 2) smaller in-sample non-target similarity to both in-sample and out-sample target similarity. In addition, we propose to establish a bilateral margin penalty for both in-sample target and non-target similarity, so that the discrimination and generalization of the deep face model are improved. To bridge the gap between training and testing of face recognition, we adapt the GCE loss into a pairwise framework by randomly replacing some class prototypes with sample features. We designate the model trained with the proposed Global Cross-Entropy loss as GFace. Extensive experiments on several public face benchmarks, including LFW, CALFW, CPLFW, CFP-FP, AgeDB, IJB-C, IJB-B, MFR-Ongoing, and MegaFace, demonstrate the superiority of GFace over other methods. Additionally, GFace exhibits robust performance in general visual recognition task.