Summary of Reverse Region-to-entity Annotation For Pixel-level Visual Entity Linking, by Zhengfei Xu et al.
Reverse Region-to-Entity Annotation for Pixel-Level Visual Entity Linking
by Zhengfei Xu, Sijia Zhao, Yanchao Hao, Xiaolong Liu, Lili Li, Yuyang Yin, Bo Li, Xi Chen, Xin Xin
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Multimedia (cs.MM)
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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 proposed Pixel-Level Visual Entity Linking (PL-VEL) task aims to improve fine-grained visual understanding by matching objects in images with entities in a knowledge base using pixel masks as input. This task addresses the limitation of previous VEL tasks relying on textual inputs, which can be challenging for complex scenes. To facilitate research on PL-VEL, the MaskOVEN-Wiki dataset is constructed through an automatic reverse region-entity annotation framework. The dataset contains over 5 million annotations and will advance visual understanding towards fine-grained. Additionally, a new attention mechanism is proposed to enhance patch-interacted attention with region-interacted attention using a visual semantic tokenization approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new task called Pixel-Level Visual Entity Linking (PL-VEL) that uses pixel masks from visual inputs to refer to objects in images. This makes it easier for people to match objects in complex scenes. To help researchers work on this task, the authors created a big dataset with over 5 million annotations that link pixel-level regions with entity-level labels. The paper also shows how a new attention mechanism can improve the accuracy of models trained on this dataset. |
Keywords
» Artificial intelligence » Attention » Entity linking » Knowledge base » Tokenization
