Learning Multiple Criteria Calibration for Generalized Zero-shot Learning

Generalized Zero-shot Learning (GZSL) seeks to identify objects from both seen and unseen classes, relying solely on labeled samples from the seen classes. One of GZSL's main challenges is the problem of bias towards seen classes, i.e., the model tends to classify all test samples as seen classes due to the data imbalance and over-fitting. To reduce the prediction bias, we proposed to train a novel Multiple Criteria Calibration (MCC) model with Confusion eliMination contrastive Embedding (CME). Specifically, we designed a novel divide-and-conquer calibration network by leveraging the learned multiple Class-Level and Instance-Level criteria to separate unseen samples from seen samples. To enhance feature separation, we further formulated supervised confusion-elimination contrastive constraints in the latent space to ensure the alignment properties of visual-semantic features and simultaneously encourage the learned features to distribute uniformly, providing a reasonable distribution for MCC and classification. After that, to adaptively mitigate bias, we can follow the learned criteria to calibrate the predictions of both the seen and unseen classes in the latent space, or leave the original predictions unchanged. Extensive experiments under GZSL settings show that the proposed method outperforms the state-of-the-art methods on four popular benchmarks.