| Challenge: | Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images, but they introduce noise and it is unclear the effect of parser errors on captioning accuracy. |
| Approach: | They investigate whether scene graphs can help image captioning . they show that a scene graph parser can boost performance almost as much as ground truth graphs . |
| Outcome: | The proposed parser can boost performance almost as much as ground truth graphs . |
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Are Scene Graphs Good Enough to Improve Image Captioning? (2020.aacl-main)
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| Challenge: | Existing image captioning models rely on object detection features to generate image descriptions, but they are noisy. |
| Approach: | They propose to use scene graphs to introduce information about object relations into captioning to improve image descriptions. |
| Outcome: | The proposed model improves image caption quality by 3.3 CIDEr compared to a strong Bottom-Up Top-Down baseline. |
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing (2023.findings-acl)
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Zhuang Li, Yuyang Chai, Terry Yue Zhuo, Lizhen Qu, Gholamreza Haffari, Fei Li, Donghong Ji, Quan Hung Tran
| Challenge: | Existing parsers that convert image captions into scene graphs often suffer from errors and inconsistency. |
| Approach: | They propose a dataset that re-annotates image captions using a new intermediate representation called FACTUAL-MR and a metric to measure scene graph similarity. |
| Outcome: | The proposed parser outperforms existing parsers in terms of faithfulness and consistency on multiple benchmark datasets. |
Scene Graph Parsing as Dependency Parsing (N18-1)
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| Challenge: | Recent studies have focused on parsing structured knowledge graphs from textual descriptions. |
| Approach: | They propose an alternative but equivalent scene graph representation that connects to dependency parses. |
| Outcome: | The proposed model outperforms best approaches on image retrieval applications. |
Transforming Visual Scene Graphs to Image Captions (2023.acl-long)
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Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Songfang Huang, Fei Huang, Zhangzikang Li, Yu Zhang
| Challenge: | Existing approaches to generate captions using image captioning are based on multi-head attention (MHA) |
| Approach: | They propose to transform scene graphs into more descriptive captions by using multi-head attention to build a Graph Neural Network (GNN) . they construct a Mixture-of-Expert (MOE)-based decoder where each expert is built on MHA for discriminating the graph embeddings to generate different kinds of words. |
| Outcome: | The proposed framework can generate captions from multiple visual features and objects . it is based on a mixture-of-expert (MOE)-based decoder based upon MHA . |
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)
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Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation. |
| Approach: | They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives. |
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Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality (2023.emnlp-main)
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| Challenge: | Recent studies have highlighted severe limitations of contrastive learning models in their ability to perform compositional reasoning over objects, attributes, and relations. |
| Approach: | They propose a graph decomposition framework and negative mining techniques to improve attribute binding and relation understanding of scene graphs. |
| Outcome: | The proposed approach improves attribute binding, relation understanding, generalization, and productivity on multiple benchmarks. |
Improving Image Captioning with Better Use of Caption (2020.acl-main)
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| Challenge: | Existing approaches to image captioning focus on visual attention, but many do not. |
| Approach: | They propose a framework that explores semantics available in captions and leverages that to enhance both image representation and caption generation. |
| Outcome: | The proposed framework outperforms baselines on the MSCOCO dataset and is state-of-the-art under a wide range of evaluation metrics. |
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs (2023.emnlp-main)
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Roei Herzig, Alon Mendelson, Leonid Karlinsky, Assaf Arbelle, Rogerio Feris, Trevor Darrell, Amir Globerson
| Challenge: | Vision and language models (VLMs) have demonstrated remarkable zero-shot (ZS) performance in a variety of tasks. |
| Approach: | They propose to integrate structured annotations into visual and textual representations to improve VLMs' understanding of compositional scenes. |
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Exploring the Impact of Vision Features in News Image Captioning (2023.findings-acl)
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| Challenge: | Recent state-of-art models can achieve competitive performance even without vision features. |
| Approach: | They conduct extensive experiments with mainstream news image captioning models to determine whether vision features contribute to the generation of captions. |
| Outcome: | The proposed models can achieve competitive performance even without vision features. |
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement (2025.emnlp-main)
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| Challenge: | Current approaches typically merge sentence-level parsing outputs for discourse input, resulting in fragmented graphs and degraded downstream performance. |
| Approach: | They propose a task for discourse-level text scene graph parsing that merges sentence-level outputs for discourse input and propose 'DiscoSG' a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs is used to test the new task. |
| Outcome: | The proposed task improves SPICE by 30% over the baseline while achieving 86 faster inference than existing models. |