Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model (2024.findings-emnlp)
Copied to clipboard
| 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. |
Similar Papers
Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)
Copied to clipboard
Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer
| Challenge: | Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown. |
| Approach: | They propose an approach to edit and re-align alt-texts associated with images using human annotation. |
| Outcome: | The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks. |
The Role of Data Curation in Image Captioning (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing image captioning models treat all samples equally, neglecting mismatched data . Several other techniques have relied on curriculum learning strategies to adapt learning to the difficulty of the task. |
| Approach: | They propose to actively curate difficult samples in datasets using curriculum learning strategies to improve captioning models. |
| Outcome: | The proposed methods outperform existing models on the Flickr30K and COCO datasets. |
CapOnImage: Context-driven Dense-Captioning on Image (2022.emnlp-main)
Copied to clipboard
| 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. |
Understanding Retrieval Robustness for Retrieval-augmented Image Captioning (2024.acl-long)
Copied to clipboard
| Challenge: | Recent retrieval-augmented models for image captioning are not perfect in practice. |
| Approach: | They propose to train a retrieval-augmented captioning model SmallCap by sampling retrieved captions from more diverse sets. |
| Outcome: | The proposed model is sensitive to tokens that appear in the majority of retrieved captions . the proposed model improves both in-domain and cross-domain performance . |
Communication breakdown: On the low mutual intelligibility between human and neural captioning (2022.emnlp-main)
Copied to clipboard
| 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 . |
Uncovering Limitations in Text-to-Image Generation: A Contrastive Approach with Structured Semantic Alignment (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new method for text-to-image generation models is proposed to address these limitations . SSA focuses on learning structured semantic embeddings across different modalities . |
| Approach: | They propose a method to evaluate text-to-image generation models using structured semantic embeddings . they propose to learn mutated prompts by substituting words with equivalent or nonequivalent alternatives . |
| Outcome: | The proposed method improves the measurement of semantic consistency of text-to-image generation models. |
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning (P18-1)
Copied to clipboard
| 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. |
Enhancing Large Vision-Language Models with Ultra-Detailed Image Caption Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing pipelines for generating high-quality, ultra-detailed image captions are limited by the scarcity of image caption data. |
| Approach: | They propose a pipeline for generating high-quality, ultra-detailed image captions that integrates both pre-processing and post-processor stages. |
| Outcome: | The proposed pipeline improves LVLMs' perception and cognitive abilities across multiple vision-language benchmarks. |
Learning from Children: Improving Image-Caption Pretraining via Curriculum (2023.findings-acl)
Copied to clipboard
| Challenge: | Image-caption pretraining is a difficult problem as it requires multiple concepts (nouns) from captions to be aligned to multiple objects in images. |
| Approach: | They propose a curriculum learning framework that uses images to align multiple concepts to multiple objects in an image. |
| Outcome: | The proposed learning framework improves over pretraining from scratch, using a pretrained image or/and text encoder, low data regime etc. |
Cross-modal Coherence Modeling for Caption Generation (2020.acl-main)
Copied to clipboard
| 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 . |