Multi-expert ensemble models for long-tailed learning typically either learn diverse generalists from the whole dataset or aggregate specialists on different subsets. However, the former is insufficient for tail classes due to the high imbalance factor of the entire dataset, while the latter may bring ambiguity in predicting unseen classes. To address these issues, we propose a novel Local and Global Logit Adjustments (LGLA) method that learns experts with full data covering all classes and enlarges the discrepancy among them by elaborated logit adjustments. LGLA consists of two core components: a Class-aware Logit Adjustment (CLA) strategy and an Adaptive Angular Weighted (AAW) loss. The CLA strategy trains multiple experts which excel at each subset using the Local Logit Adjustment (LLA). It also trains one expert specializing in an inversely long-tailed distribution through Global Logit Adjustment (GLA). Moreover, the AAW loss adopts adaptive hard sample mining with respect to different experts to further improve accuracy. Extensive experiments on popular long-tailed benchmarks manifest the superiority of LGLA over the SOTA methods.

The development of loss functions over the past few years has brought great success to face recognition. Most algorithms focus on improving the intra-class compactness of face features but ignore the inter-class separability. In this paper, we propose a method named EvenFace, which introduces a regularization variance item and a mean term of inter-class separability to further promote the even distribution of class centers on the hypersphere, thereby increasing the inter-class distance. In order to evaluate the inter-class separability, a new index is proposed to better reflect the distribution of class centers and guide the classification. By penalizing the angle between each identity and its surrounding neighbors, the resulting uniform distribution of identities enables full exploitation of the feature space, leading to discriminative face representations. Our proposed loss function can effectively boost the performance of softmax loss variants. Quantitative comparisons with other state-of-the-art methods on several benchmarks demonstrate the superiority of EvenFace.

In recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, plenty of deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace. These methods only model the distribution at the identity-level but ignore the latent relationship between frontal and profile viewpoints. Different from these methods, FCGFace takes viewpoints into consideration by modeling the distribution at both the identity-level and the viewpoint-level. At the identity-level, a softmax-based loss is employed for a relatively rough classification. At the viewpoint-level, centers of frontal face features are defined to guide the optimization conducted in a more refined way. Specifically, our FCGFace is capable of adaptively adjusting the distribution of profile face features and narrowing the gap between them and frontal face features during different training stages to form compact identity clusters. Extensive experimental results on popular benchmarks, including cross-pose datasets (CFP-FP, CPLFW, VGGFace2-FP, and Multi-PIE) and non-cross-pose datasets (YTF, LFW, AgeDB-30, CALFW, IJB-B, IJB-C, and RFW), have demonstrated the superiority of our FCGFace over the SOTA competitors.