| Challenge: | a traditional image captioning task uses generic reference captions to provide textual information about images. |
| Approach: | They propose a task that uses question-answer pairs to provide visual information instead of generic reference captions. |
| Outcome: | The proposed captioning with a purpose task can be tailored to meet user needs . question-answer pairs are used as a source of supervision for learning visual information needs a new task is proposed . |
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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. |
Generating Question Relevant Captions to Aid Visual Question Answering (P19-1)
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| Challenge: | Visual question answering and image captioning require a shared body of general knowledge connecting language and vision. |
| Approach: | They propose a method that exploits a shared body of general knowledge connecting language and vision by jointly generating captions. |
| Outcome: | The proposed approach obtains state-of-the-art performance on the VQA v2 challenge . it uses human annotated captions to generate question-relevant captions . |
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. |
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. |
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. |
Concadia: Towards Image-Based Text Generation with a Purpose (2022.emnlp-main)
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| Challenge: | Existing models fail to generate fluent, truthful text, despite excellent results on benchmark datasets . current systems fail to produce texts that are useful in practice, authors argue . |
| Approach: | They propose to distinguish descriptions from captions based on their communicative roles . descriptions focus on visual features and are meant to replace an image . authors characterize commonalities and differences between descriptions and captions in a Wikipedia corpus . |
| Outcome: | The proposed model can generate fluent, truthful texts in a wide range of scenarios . it can also generate captions that are used to make an image accessible to users who can't see them . |
Multi-Modal Image Captioning for the Visually Impaired (2021.naacl-srw)
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| Challenge: | Current captioning models for blind people do not use textual data present in the image when generating captions. |
| Approach: | They propose to use text detected in the image as an input feature in captions . they also use a pointer-generator network to copy detected text to the caption . |
| Outcome: | The proposed system outperforms existing models on the VizWiz dataset, showing a 35% and 16.2% performance improvement. |
CapOnImage: Context-driven Dense-Captioning on Image (2022.emnlp-main)
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| Challenge: | Existing image captioning systems generate narrative captions for images, which are spatially detached from the image in presentation. |
| Approach: | They propose a task called captioning on image which generatesense captions at different locations of the image based on contextual information. |
| Outcome: | The proposed model achieves the best results in both captioning accuracy and diversity aspects. |
QACE: Asking Questions to Evaluate an Image Caption (2021.findings-emnlp)
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| Challenge: | Existing metric for image captioning evaluation is based on n-gram similarity metrics but these fail to capture semantic errors in captions. |
| Approach: | They propose a new metric based on Question Answering for Caption Evaluation to evaluate image captioning based upon Question Generation and Question Answers systems. |
| Outcome: | The proposed metric is multi-modal, reference-less and explainable. |
Augmenting Image Question Answering Dataset by Exploiting Image Captions (L18-1)
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| Challenge: | Image question answering requires large amounts of human-annotated data to achieve optimal performance. |
| Approach: | They propose a framework to augment training data by generating additional examples from unannotated pairs of an image and captions. |
| Outcome: | The proposed framework augments training data by generating additional examples from unannotated pairs of an image and captions. |