Detailed Descriptions of Connected and Contrasting Images for Evaluating Text-to-Image and Image-to-Text Models

Kernekoncepter

The authors introduce DOCCI, a new dataset of 15k images with detailed human-annotated descriptions, to serve as a challenging benchmark for evaluating the capabilities of text-to-image and image-to-text generation models.

Resumé

The authors introduce Descriptions of Connected and Contrasting Images (DOCCI), a new vision-language dataset that consists of 15k newly curated images with detailed descriptions annotated by humans. The images were intentionally collected and curated to capture key challenges for text-to-image (T2I) models, such as spatial relationships, counting, text rendering, and world knowledge.
The dataset construction process involves three stages: 1) extracting key aspects like objects and attributes from the images and writing short descriptions, 2) consolidating the short descriptions into one detailed, coherent natural language description, and 3) enriching the description by adding important details. The authors implement rigorous quality control steps to ensure high-quality annotations.
The authors evaluate current highly-performant T2I and image-to-text (I2T) models using DOCCI, conducting both quantitative and qualitative analyses. They find that a PaLI 5B model finetuned on DOCCI can greatly improve I2T generation, outperforming larger models like LLaVA-1.5 7B and InstructBLIP 7B. However, the authors also show that current T2I models still exhibit numerous error modes related to spatial relationships, counting, and text rendering. They highlight the limitations of automatic metrics like FID and CLIPScore, which do not align with human evaluation results.
Furthermore, the authors compare the detailed DOCCI descriptions to those generated by the powerful GPT-4v model, finding that the human-written descriptions still contain more precise details despite GPT-4v's fluency. This demonstrates that there are still important gaps between machine-generated and human-written descriptions.
Overall, the DOCCI dataset provides a challenging and comprehensive benchmark for evaluating the capabilities of T2I and I2T models, highlighting their current limitations and guiding future research.

Statistik

The average length of DOCCI descriptions is 135.9 words, substantially longer than datasets like COCO (11.3 words) and Stanford Visual Paragraphs (68.5 words).
DOCCI descriptions cover a wide range of challenges, with 99.9% mentioning spatial relationships, 97.3% describing color attributes, and 54.6% including counting.
The DOCCI dataset is split into 9,647 train, 5,000 test, 100 qualification-dev, and 100 qualification-test sets.

Citater

"DOCCI covers a wide range of outstanding issues of T2I models."
"Equipped with the newly-curated images and detailed descriptions, DOCCI covers a wide range of outstanding issues of T2I models."
"We show that the limited input length of most T2I models is problematic as it causes significant parts of the description (i.e., prompt) to be omitted, making it impossible to include those details in the generated image."

Vigtigste indsigter udtrukket fra

DOCCI: Descriptions of Connected and Contrasting Images

by Yasumasa Ono... kl. arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19753.pdf

Dybere Forespørgsler

How can the DOCCI dataset be expanded to include more diverse images and scenes beyond the current geographical and subject biases?

Expanding the DOCCI dataset to include more diverse images and scenes can be achieved through several strategies:

Collaboration with Multiple Contributors: Encouraging contributions from a diverse group of photographers and researchers can help capture a wider range of images from various locations and perspectives. This collaborative approach can bring in images that reflect different cultures, landscapes, and subjects.

Crowdsourcing and Community Involvement: Leveraging crowdsourcing platforms or engaging with the research community can help source images from a broader geographical area. By involving a larger pool of contributors, the dataset can encompass a more diverse set of scenes and subjects.

Targeted Image Collection: Actively seeking out specific types of images or scenes that are currently underrepresented in the dataset can help fill in the gaps. This could involve targeted photo shoots or collaborations with photographers specializing in certain themes or locations.

Incorporating User-Generated Content: Allowing users to submit their images for inclusion in the dataset can bring in a wide variety of perspectives and subjects. This approach can help capture unique and diverse images that may not be easily accessible through traditional means.

Quality Control and Annotation Guidelines: Ensuring consistent annotation guidelines and quality control measures will be crucial when expanding the dataset. Clear instructions for annotators on how to describe diverse images accurately will help maintain the dataset's integrity and relevance.

By implementing these strategies, the DOCCI dataset can evolve to include a more comprehensive and diverse collection of images, enriching its utility for research and development in the vision-language domain.

How can the detailed annotations in DOCCI be leveraged to develop new text-to-image generation models that can better capture fine-grained visual details?

The detailed annotations in the DOCCI dataset provide a rich source of information that can be leveraged to enhance text-to-image generation models in the following ways:

Fine-Grained Feature Extraction: The annotations can be used to train models to extract fine-grained visual features from textual descriptions. By aligning detailed annotations with corresponding images, models can learn to capture intricate visual details mentioned in the text.

Multi-Modal Fusion: The annotations can facilitate multi-modal fusion techniques, where textual descriptions are combined with visual features to generate more detailed and accurate images. Models can learn to integrate textual cues with visual information to produce images that align closely with the annotations.

Attention Mechanisms: The annotations can guide the development of attention mechanisms in text-to-image models. By highlighting specific visual elements mentioned in the annotations, models can focus on relevant parts of the image during the generation process, improving the fidelity of the generated images.

Evaluation and Benchmarking: The detailed annotations can serve as a benchmark for evaluating the performance of text-to-image models in capturing fine-grained visual details. Models can be tested against the annotations to assess their ability to reproduce specific visual features mentioned in the text accurately.

Transfer Learning: Pre-training models on the annotated DOCCI dataset can help them learn to generate images with finer details. By leveraging the detailed annotations during pre-training, models can develop a better understanding of the relationship between textual descriptions and visual content.

By utilizing the detailed annotations in the DOCCI dataset in these ways, researchers can advance the development of text-to-image generation models that excel in capturing fine-grained visual details with precision and accuracy.

What other applications beyond text-to-image and image-to-text generation could the DOCCI dataset enable, such as visual reasoning or multimodal understanding?

The DOCCI dataset, with its detailed annotations and diverse set of images, can enable a range of applications beyond text-to-image and image-to-text generation, including:

Visual Reasoning: The dataset can be used to train models for visual reasoning tasks, where AI systems analyze and answer questions about images. The detailed annotations can provide valuable context for developing models that can reason about visual content effectively.

Multimodal Understanding: DOCCI can facilitate research in multimodal understanding, where models learn to interpret and generate information across different modalities such as text and images. The dataset's annotations can support the development of models that can understand and generate content in multiple forms.

Content Creation and Storytelling: By leveraging the rich annotations in DOCCI, researchers can explore applications in content creation and storytelling. Models trained on the dataset can generate multimedia content, combining text and images to create engaging narratives and visual stories.

Visual Search and Retrieval: The dataset can be used to develop visual search and retrieval systems that can accurately match textual queries with relevant images. Models trained on DOCCI can learn to understand the nuances of textual descriptions and retrieve corresponding images effectively.

Artistic Rendering and Style Transfer: Researchers can explore applications in artistic rendering and style transfer using the DOCCI dataset. Models can learn to generate images that reflect specific artistic styles or transfer the visual characteristics of one image to another based on detailed annotations.

By exploring these diverse applications, the DOCCI dataset can serve as a versatile resource for advancing research in visual reasoning, multimodal understanding, content creation, and other areas that require a deep understanding of both text and images.