Residual-Prototype Generating Network for Generalized Zero-Shot Learning

Zhang, Zeqing; Li, Xiaofan; Ma, Tai; Gao, Zuodong; Li, Cuihua; Lin, Weiwei

doi:10.3390/math10193587

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…In our analysis, we are still considering the methods quoted above, while also adding many other approaches benchmarked in the more challenging GZSL setup. In fact, we also consider approaches designed for few-shot learning and casted to the zero-shot setting (such as CRnet [46]). We account for approaches which tackle zero-shot learning by an attention mechanism over the attributes used to describe each categories (LFGAA [27], ZSL-OCD [20] and DAZLE [17]).…”

Section: Comparisons With the State-of-the-artmentioning

confidence: 99%

No Adversaries to Zero-Shot Learning: Distilling an Ensemble of Gaussian Feature Generators

Cavazza

Murino

Bue

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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In zero-shot learning (ZSL), the task of recognizing unseen categories when no data for training is available, state-of-the-art methods generate visual features from semantic auxiliary information (e.g., attributes). In this work, we propose a valid alternative (simpler, yet better scoring) to fulfill the very same task. We observe that, if first-and second-order statistics of the classes to be recognized were known, sampling from Gaussian distributions would synthesize visual features that are almost identical to the real ones as per classification purposes. We propose a novel mathematical framework to estimate first-and second-order statistics, even for unseen classes: our framework builds upon prior compatibility functions for ZSL and does not require additional training. Endowed with such statistics, we take advantage of a pool of class-specific Gaussian distributions to solve the feature generation stage through sampling. We exploit an ensemble mechanism to aggregate a pool of softmax classifiers, each trained in a one-seen-class-out fashion to better balance the performance over seen and unseen classes. Neural distillation is finally applied to fuse the ensemble into a single architecture which can perform inference through one forward pass only. Our method, termed Distilled Ensemble of Gaussian Generators, scores favorably with respect to state-of-the-art works.

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