Summary of Syncmask: Synchronized Attentional Masking For Fashion-centric Vision-language Pretraining, by Chull Hwan Song et al.
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining
by Chull Hwan Song, Taebaek Hwang, Jooyoung Yoon, Shunghyun Choi, Yeong Hyeon Gu
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called Synchronized attentional Masking (SyncMask) to address the issue of disparity between image and text information in fashion domain datasets. Conventional vision-language models (VLMs) struggle to align fine-grained visual and textual features due to mismatched masks that fail to pinpoint co-occurring elements. The proposed method harnesses cross-attentional features from a momentum model to synchronize attentional masking, ensuring precise alignment between modalities. Additionally, the authors enhance grouped batch sampling with semi-hard negatives to mitigate false negative issues in downstream tasks. Experimental results demonstrate the effectiveness of SyncMask, outperforming existing methods in three downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with fashion datasets where some details are only shown in text but not in pictures. This makes it hard for computer models to learn from these datasets. The authors propose a new way to make masks that point to the specific parts of the picture and text where the information matches. They also improve how batches are sampled to avoid mistakes. The results show that this approach works better than existing methods. |
Keywords
» Artificial intelligence » Alignment
