| Challenge: | Traditionally, image captioning models are trained to generate a single “best’ (most like a reference) image caption. |
| Approach: | They propose a method to generate a single caption that captures high-level details from several annotator viewpoints. |
| Outcome: | The proposed method outperforms baseline SOTA models and improves the performance of automated recall systems by up to 84%. |
Similar Papers
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. |
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model (2024.findings-emnlp)
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| Challenge: | Recent advances in text-to-image models have demonstrated remarkable capabilities in image synthesis. |
| Approach: | They analyze the critical role of caption precision and recall in text-to-image model training. |
| Outcome: | The proposed model trains with synthetic captions that show similar behavior to those trained on human-annotated captions. |
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. |
Pragmatically Informative Image Captioning with Character-Level Inference (N18-2)
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| Challenge: | a neural image captioner and a Rational Speech Acts (RSA) model are pragmatically informative . previous attempts to combine RSA with neural image-captioning require an inference which normalizes over the entire set of possible utterances. |
| Approach: | They propose a neural image captioner with a Rational Speech Acts model to make it pragmatically informative. |
| Outcome: | The proposed system outperforms a non-pragmatic baseline and word-level RSA captioner on a word-based model. |
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. |
Informative Image Captioning with External Sources of Information (P19-1)
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| 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. |
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning (P18-1)
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| Challenge: | Practical applications of automatic image description systems include leveraging descriptions for image indexing or retrieval, and helping those with visual impairments by transforming visual signals into information that can be communicated via text-to-speech technology. |
| Approach: | They propose to extract and filter image caption annotations from billions of webpages and use them to train models. |
| Outcome: | The proposed model architectures perform better when trained on the Conceptual Captions dataset. |
Transparent Human Evaluation for Image Captioning (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
| Challenge: | Recent work has demonstrated that image captioning is a complex task that requires a large amount of human input. |
| Approach: | They develop a human evaluation protocol for image captioning models based on machine- and human-generated captions on the MSCOCO dataset. |
| Outcome: | The proposed model improves CLIPScore, a recent metric that uses image features, and improves human judgments because it is more sensitive to recall. |
TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning (2024.findings-emnlp)
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| Challenge: | Existing approaches to enhance zero-shot abilities in image captioning fail with fine-grained datasets. |
| Approach: | They propose a method to enhance captions with additional object-part details using object detector proposals and natural language processing techniques. |
| Outcome: | The proposed method improves performance on fine-grained datasets and improves on existing methods. |
Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022.emnlp-main)
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| Challenge: | 0-shot performance of a neural caption-based image retriever is higher when fed captions from a human-produced caption generator . despite the fact that the caption generator does not take the set of distractor images into account, this performance is only marginally above chance level. |
| Approach: | They compare the 0-shot performance of a neural caption-based image retriever with captions from a human-produced captioner. |
| Outcome: | The proposed model performs better when given human-produced captions or neural captions . the best pre-trained model perform better when fed captions produced by an out-of-the-box model . |