Summary of Simvg: a Simple Framework For Visual Grounding with Decoupled Multi-modal Fusion, by Ming Dai et al.
SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion
by Ming Dai, Lingfeng Yang, Yihao Xu, Zhenhua Feng, Wankou Yang
First submitted to arxiv on: 26 Sep 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 This paper presents a novel transformer-based framework, SimVG, for visual grounding, which involves grounding descriptive sentences to the corresponding regions of an image. The proposed method decouples visual-linguistic feature fusion from downstream tasks by leveraging existing multimodal pre-trained models and incorporating additional object tokens. A dynamic weight-balance distillation method is also designed in the multi-branch synchronous learning process to enhance representation capability. Experiments on six widely used datasets demonstrate the superiority of SimVG, achieving improvements in efficiency and convergence speed while attaining new state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual grounding helps us understand what an image describes. Most current methods are good at simple sentences but struggle with complex ones. This paper proposes a new way to solve this problem using transformers. It’s like a shortcut that makes the method faster and more accurate. The authors test their approach on six different datasets and show it works better than existing methods. |
Keywords
» Artificial intelligence » Distillation » Grounding » Transformer
