Researches using margin based comparison loss demonstrate the effectiveness of penalizing the distance between face feature and their corresponding class centers. Despite their popularity and excellent performance, they do not explicitly encourage the generic embedding learning for an open set recognition problem. In this paper, we analyse margin based softmax loss in probability view. With this perspective, we propose two general principles: 1) monotonically decreasing and 2) margin probability penalty, for designing new margin loss functions. Unlike methods optimized with single comparison metric, we provide a new perspective to treat open set face recognition as a problem of information transmission. And the generalization capability for face embedding is gained with more clean information. An auto-encoder architecture called Linear-Auto-TS-Encoder(LATSE) is proposed to corroborate this finding. Extensive experiments on several benchmarks demonstrate that LATSE help face embedding to gain more generalization capability and it boost the single model performance with open training dataset to more than 99% on MegaFace test.

Clothing parsing is significant to many clothing applications. Recently, a lot of clothing parsing methods have been presented, which explore the innovation of the parsing pipeline or try to find more specific prior information. Although these methods perform well in some benchmarks, a few challenging problems have not been solved yet, such as the complicated mutual interference among labels. In this study, the authors propose a Conditional Progressive Network to parse clothing in different scales and prevent the mutual interference among labels. The authors’ solution consists of three sub‐networks, including Conditional Parsing Network (CPN), Pose Estimation Network (PEN) and Label Transform Network (LTN). Specifically, the CPN module generates the intermediate parsing result in the form of the multiple progressive stages, which combines with the previous outputs in each stage and the specific prior conditions. The PEN module provides a series of heat maps about the human pose information. The LTN module suppresses the redundant labels to avoid the mutual interference among labels. They demonstrate their solution in parsing the fashion clothing cases on the ATR and the Fashion dataset. In their experiments, their method obtains a better performance than the state‐of‐the‐art methods.

We propose a novel dimensionality reduction approach based on the gradient of the regression function. Our approach is conceptually similar to Principal Component Analysis, however instead of seeking a low dimensional representation of the predictors that preserve the sample variance, we project onto a basis that preserves those predictors which induce the greatest change in the response. Our approach has the benefits of being simple and easy to implement and interpret, while still remaining very competitive with sophisticated state-of-the-art approaches.

Human parsing, which segments a human-centric image into pixel-wise categorization, has a wide range of applications. However, none of the existing methods can productively solve the issue of label parsing fragmentation due to confused and complicated annotations. In this paper, we propose a novel Trusted Guidance Pyramid Network (TGPNet) to address this limitation. Based on a pyramid architecture, we design a Pyramid Residual Pooling (PRP) module setting at the end of a bottom-up approach to capture both global and local level context. In the top-down approach, we propose a Trusted Guidance Multi-scale Supervision (TGMS) that efficiently integrates and supervises multi-scale contextual information. Furthermore, we present a simple yet powerful Trusted Guidance Framework (TGF) which imposes global-level semantics into parsing results directly without extra ground truth labels in model training. Extensive experiments on two public human parsing benchmarks well demonstrate that our TGPNet has a strong ability in solving label parsing fragmentation problem and has an obtained improvement than other methods.