| Challenge: | Current captioning models are trained to generate captions that only contain common object names, thus falling short on an important “informativeness” dimension. |
| Approach: | They propose a mechanism for integrating image information and fine-grained labels into a caption that describes the image in a fluent and informative manner. |
| Outcome: | The proposed model integrates image information with fine-grained labels to produce fluent captions . it can control the appearance of these labels in the output, resulting in fluent and informative captions. |
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Entity-aware Image Caption Generation (D18-1)
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| Challenge: | Existing image captioning approaches generate generic descriptions of visual content and ignore background information. |
| Approach: | They propose a task which generates informative image captions using images and hashtags as input. |
| Outcome: | The proposed model outperforms unimodal baselines significantly with evaluation metrics on a dataset from Flickr. |
Image Caption Generation for News Articles (2020.coling-main)
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| Challenge: | Existing work on news-image captioning requires a joint understanding of image and text. |
| Approach: | They propose a Transformer model that integrates text and image modalities and attends to textual features from visual features in generating a caption. |
| Outcome: | The proposed model outperforms the state-of-the-art model and improves the quality of news-image captions. |
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization (2022.findings-emnlp)
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| Challenge: | Combining visual modality with pretrained language models has been effective for descriptive tasks such as image captioning. |
| Approach: | They ask: do multimodal models combine visual and visual adapted language models? they find that CLIP image representations and scaling of language models do not consistently improve self-rationalization in multimodal tasks. |
| Outcome: | The proposed model types do not consistently improve self-rationalization in multimodal tasks. |
Cross-modal Coherence Modeling for Caption Generation (2020.acl-main)
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| Challenge: | Existing methods for image captioning do not guarantee consistent image-text relations . current models do not provide enough data for training robust captioning models . |
| Approach: | They use an annotation protocol specifically devised for capturing image–caption coherence relations to study image captioning. |
| Outcome: | The proposed protocol improves image captioning models with coherence relations . the dataset is large enough to alleviate content hallucinations, the authors show . |
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. |
What Makes for Good Image Captions? (2025.findings-emnlp)
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| Challenge: | a formal information-theoretic framework is developed for image captioning . the pyramid of captions is a method that generates enriched captions by integrating local and global visual information. |
| Approach: | They propose a formal information-theoretic framework for image captioning . they propose 'Pyramid of Captions' method that generates enriched captions . |
| Outcome: | The proposed framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
Focus! Relevant and Sufficient Context Selection for News Image Captioning (2022.findings-emnlp)
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| Challenge: | Recent work only coarsely leverages the article to extract the necessary context, which makes it difficult for models to identify relevant events and named entities. |
| Approach: | They propose to use a vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model. |
| Outcome: | The proposed model significantly improves on existing models and achieves state-of-the-art on multiple benchmarks. |
Geo-Aware Image Caption Generation (2020.coling-main)
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| Challenge: | Standard image caption generation systems do not take contextual information or world knowledge into account. |
| Approach: | They propose to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. |
| Outcome: | The proposed system achieves significant improvements on a dataset that contains contextualized captions and geographic metadata and improves BLEU, ROUGE, METEOR and CIDEr scores. |
Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)
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| Challenge: | Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information. |
| Approach: | They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions . |
| Outcome: | The proposed method is scalable and generates detailed and context-aware captions on large-scale audio datasets. |