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Zero-shot capability has been considered as a new revolution of deeplearning, letting machines work on tasks without curated training data. As agood start and the only existing outcome of zero-shot image captioning (IC),ZeroCap abandons supervised training and sequentially searches every word inthe caption using the knowledge of large-scale pretrained models. Thougheffective, its autoregressive generation and gradient-directed searchingmechanism limit the diversity of captions and inference speed, respectively.Moreover, ZeroCap does not consider the controllability issue of zero-shot IC.To move forward, we propose a framework for Controllable Zero-shot IC, namedConZIC. The core of ConZIC is a novel sampling-based non-autoregressivelanguage model named GibbsBERT, which can generate and continuously polishevery word. Extensive quantitative and qualitative results demonstrate thesuperior performance of our proposed ConZIC for both zero-shot IC andcontrollable zero-shot IC. Especially, ConZIC achieves about 5x fastergeneration speed than ZeroCap, and about 1.5x higher diversity scores, withaccurate generation given different control signals.

ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing