Zero-shot recognition (ZSR) deals with the problem of predicting class labels
for target domain instances based on source domain side information (e.g.
attributes) of unseen classes. We formulate ZSR as a binary prediction problem.
Our resulting classifier is class-independent. It takes an arbitrary pair of
source and target domain instances as input and predicts whether or not they
come from the same class, i.e. whether there is a match. We model the posterior
probability of a match since it is a sufficient statistic and propose a latent
probabilistic model in this context. We develop a joint discriminative learning
framework based on dictionary learning to jointly learn the parameters of our
model for both domains, which ultimately leads to our class-independent
classifier. Many of the existing embedding methods can be viewed as special
cases of our probabilistic model. On ZSR our method shows 4.90\% improvement
over the state-of-the-art in accuracy averaged across four benchmark datasets.
We also adapt ZSR method for zero-shot retrieval and show 22.45\% improvement
accordingly in mean average precision (mAP).

该研究提出了一种无类别依赖的零样本识别分类器，利用二分类预测问题建模后使用词典学习，通过概率模型进行匹配概率的建模，取得了四个基准数据集平均准确率上 4.9% 的提升，在零样本检索任务中则获得了 22.45% 的平均精确度提升。