Summary of How to Understand “support”? An Implicit-enhanced Causal Inference Approach For Weakly-supervised Phrase Grounding, by Jiamin Luo et al.
How to Understand “Support”? An Implicit-enhanced Causal Inference Approach for Weakly-supervised Phrase Grounding
by Jiamin Luo, Jianing Zhao, Jingjing Wang, Guodong Zhou
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 Medium Difficulty summary: This paper proposes Implicit-Enhanced Causal Inference (IECI) to address challenges in Weakly-supervised Phrase Grounding (WPG), an emerging task that infers fine-grained phrase-region matching using coarse-grained sentence-image pairs. The IECI approach leverages intervention and counterfactual techniques to model implicit relations between phrases and regions, going beyond explicit matches. A high-quality dataset is annotated for evaluation, showing IECI outperforms state-of-the-art baselines, including advanced multimodal language models (LLMs) by a significant margin on this specific task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper solves a problem called phrase grounding, where computers try to match phrases with images. Most previous studies didn’t consider the hidden relationships between phrases and images. The new approach, IECI, helps computers understand these connections better. It uses special techniques to analyze data and improve performance. The results show that IECI is much better than other approaches at this task. This could help us evaluate how well language models can understand images. |
Keywords
* Artificial intelligence * Grounding * Inference * Supervised
