SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining
This work addresses a domain-specific issue in fashion-centric vision-language pretraining, offering an incremental improvement for better alignment in datasets with mismatched modalities.
The paper tackled the problem of mismatched image-text pairs in fashion datasets, where multiple images share one text, by proposing SyncMask to generate synchronized masks for co-occurring patches and tokens, resulting in outperforming existing methods on three downstream tasks.
Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However, in fashion domain, datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text, leading to cases where some textual details are not visible in individual images. This mismatch, particularly when non-co-occurring elements are masked, undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling, thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem, we propose Synchronized attentional Masking (SyncMask), which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model, ensuring a precise alignment between the two modalities. Additionally, we enhance grouped batch sampling with semi-hard negatives, effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach, outperforming existing methods in three downstream tasks.