| Challenge: | Existing approaches to image captioning use explicit object detectors as an intermediate step, but they bypass the explicit detection phase and instead generate captions directly from image embeddings. |
| Approach: | They argue that explicit detections provide rich semantic information and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. |
| Outcome: | The proposed methods can be used to understand why end-to-end captioning systems work well. |
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| Challenge: | Existing models for vision-to-language tasks do not understand images sufficiently, despite their impressive performance. |
| Approach: | They propose to use explicit object information to solve foiled caption detection problem . they propose to replace a word in caption with a semantically similar word . |
| Outcome: | The proposed model achieves state-of-the-art on a recently published dataset with scores exceeding those achieved by humans on the task. |
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. |
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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. |
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ReCAP: Semantic Role Enhanced Caption Generation (2024.lrec-main)
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| Challenge: | Current vision language models lack specificity and overlook various aspects of the image. |
| Approach: | They propose to use semantic roles as control signals to guide captions to specific argument structures by focusing on specific objects and their associated semantic roles instead of general descriptions. |
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Visual Referring Expression Recognition: What Do Systems Actually Learn? (N18-2)
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| Challenge: | Existing systems for referring expression recognition ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. |
| Approach: | They propose to use a system trained on the input image without the input referring expression to achieve a precision of 71.2% in top-2 predictions. |
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Synonym relations affect object detection learned on vision-language data (2024.findings-naacl)
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| Challenge: | a recent study shows that vision-language models that accept textual input are not robust to variations in how input is provided. |
| Approach: | They propose two approaches to improve vision-language object detectors' performance . they use back-translation and class embedding enrichment to improve their models . |
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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. |
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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. |
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Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)
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| Challenge: | In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. |
| Approach: | They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models. |
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O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)
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| Challenge: | Existing methods for video captioning consider a sequence of frames and biases towards focused objects. |
| Approach: | They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption . |
| Outcome: | The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed. |