Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP imposes a bidirectional constraints on global representation of entire images and sentences. Although IC conducts an unidirectional image-to-text generation on local representation, it lacks any constraint on local text-to-image reconstruction, which limits the ability to understand images at a fine-grained level when aligned with texts. To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels. Specifically, we expand a Text-Guided Masked Image Modeling (TG-MIM) head based on ITC and IC heads. The improved SyCoCa can further leverage textual cues to reconstruct contextual images and visual cues to predict textual contents. When implementing bidirectional local interactions, the local contents of images tend to be cluttered or unrelated to their textual descriptions. Thus, we employ an attentive masking strategy to select effective image patches for interaction. Extensive experiments on five vision-language tasks, including image-text retrieval, image-captioning, visual question answering, and zero-shot/finetuned image classification, validate the effectiveness of our proposed method.

当前视觉语言模型研究的基础主题是语言和视觉之间的多模态对齐。对比式字幕生成器 (CoCa) 是一种代表性方法，它将对比语言-图像预训练 (CLIP) 和图像字幕 (IC) 集成到统一框架中，取得了令人印象深刻的结果。本文提出了一种称为对称式对比式字幕生成器 (SyCoCa) 的方法，从全局和局部表达水平上引入图像和文本交互，以实现多模态对齐。在实验中，我们验证了所提出方法的有效性。

SyCoCa: 对称化的关注屏蔽对齐的对比式字幕生成器