Hybrid Regularization with Elastic Net and Linear Discriminant Analysis for ZeroShot Image Recognition Zeroshot learning (ZSL) is the process of recognizing unseen samples from their related classes. Generally, ZSL is realized with the help of some predefined semantic information via projecting high dimensional visual features of data samples and classrelated semantic vectors into a common embedding space. Although classification can be simply decided through the nearestneighbor strategy, it usually suffers from problems of domain shift and hubness. In order to address these challenges, majority of researches have introduced regularization with some existing norms, such as lasso or ridge, to constrain the learned embedding. However, the sparse estimation of lasso may cause underfitting of training data, while ridge may introduce bias in the embedding space. In order to resolve these problems, this paper proposes a novel hybrid regularization approach by leveraging elastic net and linear discriminant analysis, and formulates a unified objective function that can be solved efficiently via a synchronous optimization strategy. The proposed method is evaluated on several benchmark image datasets for the task of generalized ZSL. The obtained results demonstrate the superiority of the proposed method over simple regularized methods as well as several previous models.

Modeling Inter and IntraClass Relations in the Triplet Loss for ZeroShot Learning Recognizing visual unseen classes, i.e. for which no training data is available, is known as Zero Shot Learning (ZSL). Some of the best performing methods apply the triplet loss to seen classes to learn a mapping between visual representations of images and attribute vectors that constitute class prototypes. They nevertheless make several implicit assumptions that limit their performance on real use cases, particularly with finegrained datasets comprising a large number of classes. We identify three of these assumptions and put forward corresponding novel contributions to address them. Our approach consists in taking into account both interclass and intraclass relations, respectively by being more permissive with confusions between similar classes, and by penalizing visual samples which are atypical to their class. The approach is tested on four datasets, including the largescale ImageNet, and exhibits performances significantly above recent methods, even generative methods based on more restrictive hypotheses.