Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment
This work addresses zero-shot image captioning for vision-language tasks, offering an incremental improvement by enhancing CLIP's visual attention.
The paper tackled the problem of zero-shot image captioning by addressing the contextual language prior in CLIP, which overly relies on textual context and ignores visual information, and proposed Cross-modal Language Models with Anchor Augment to improve attention to fine-grained details, achieving promising performance on MS COCO and Flickr 30K datasets.
CLIP (Contrastive Language-Image Pre-Training) has shown remarkable zero-shot transfer capabilities in cross-modal correlation tasks such as visual classification and image retrieval. However, its performance in cross-modal generation tasks like zero-shot image captioning remains unsatisfied. In this work, we discuss that directly employing CLIP for zero-shot image captioning relies more on the textual modality in context and largely ignores the visual information, which we call \emph{contextual language prior}. To address this, we propose Cross-modal Language Models (CLMs) to facilitate unsupervised cross-modal learning. We further propose Anchor Augment to guide the generative model's attention to the fine-grained information in the representation of CLIP. Experiments on MS COCO and Flickr 30K validate the promising performance of proposed approach in both captioning quality and computational efficiency.