Generalizable Entity Grounding via Assistance of Large Language Model
- URL: http://arxiv.org/abs/2402.02555v1
- Date: Sun, 4 Feb 2024 16:06:05 GMT
- Title: Generalizable Entity Grounding via Assistance of Large Language Model
- Authors: Lu Qi, Yi-Wen Chen, Lehan Yang, Tiancheng Shen, Xiangtai Li, Weidong
Guo, Yu Xu, Ming-Hsuan Yang
- Abstract summary: We propose a novel approach to densely ground visual entities from a long caption.
We leverage a large multimodal model to extract semantic nouns, a class-a segmentation model to generate entity-level segmentation, and a multi-modal feature fusion module to associate each semantic noun with its corresponding segmentation mask.
- Score: 77.07759442298666
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In this work, we propose a novel approach to densely ground visual entities
from a long caption. We leverage a large multimodal model (LMM) to extract
semantic nouns, a class-agnostic segmentation model to generate entity-level
segmentation, and the proposed multi-modal feature fusion module to associate
each semantic noun with its corresponding segmentation mask. Additionally, we
introduce a strategy of encoding entity segmentation masks into a colormap,
enabling the preservation of fine-grained predictions from features of
high-resolution masks. This approach allows us to extract visual features from
low-resolution images using the CLIP vision encoder in the LMM, which is more
computationally efficient than existing approaches that use an additional
encoder for high-resolution images. Our comprehensive experiments demonstrate
the superiority of our method, outperforming state-of-the-art techniques on
three tasks, including panoptic narrative grounding, referring expression
segmentation, and panoptic segmentation.
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