DreamLIP: Language-Image Pre-training with Long Captions (2403.17007v1)

Abstract: Language-image pre-training largely relies on how precisely and thoroughly a text describes its paired image. In practice, however, the contents of an image can be so rich that well describing them requires lengthy captions (e.g., with 10 sentences), which are usually missing in existing datasets. Consequently, there are currently no clear evidences on whether and how language-image pre-training could benefit from long captions. To figure this out, we first re-caption 30M images with detailed descriptions using a pre-trained Multi-modality LLM (MLLM), and then study the usage of the resulting captions under a contrastive learning framework. We observe that, each sentence within a long caption is very likely to describe the image partially (e.g., an object). Motivated by this, we propose to dynamically sample sub-captions from the text label to construct multiple positive pairs, and introduce a grouping loss to match the embeddings of each sub-caption with its corresponding local image patches in a self-supervised manner. Experimental results on a wide rage of downstream tasks demonstrate the consistent superiority of our method, termed DreamLIP, over previous alternatives, highlighting its fine-grained representational capacity. It is noteworthy that, on the tasks of image-text retrieval and semantic segmentation, our model trained with 30M image-text pairs achieves on par or even better performance than CLIP trained with 400M pairs. Project page is available at https://zyf0619sjtu.github.io/dream-lip.

Language-Image Pre-Training Using Long Captions: A Detailed Examination

The paper "DreamLIP: Language-Image Pre-training with Long Captions" by Zheng et al. focuses on enhancing the efficacy of language-image pre-training via the use of long captions. Traditional approaches in this domain typically rely on concise captions to describe images, often failing to capture the full richness of visual data. This research body explores the use of detailed captions, which are automatically generated using Multi-modality LLMs (MLLMs), to improve the granularity and accuracy of image representation within pre-training frameworks.

Methodological Innovations

A primary contribution of this work is the proposal of a novel framework that strategically leverages long captions for encoding fine-grained details about images. The authors undertake the recaptioning of 30 million images using a pre-trained MLLM to furnish lengthy textual descriptions capable of excavating rich semantic content from the images. They emphasize that these detailed captions consist of multiple sentences, each potentially highlighting specific aspects of the image.

The authors' approach involves dynamically sampling sub-captions from these long captions to form multiple positive training pairs. This methodology is integrated into a contrastive learning framework through a multi-positive loss strategy which enhances the alignment between image features and their corresponding textual embeddings. Additionally, a unique grouping loss is introduced to associate sub-caption embeddings with their relevant local image patches, seeking alignment even at a granular level.

Key Results and Findings

Empirical evaluations of the proposed method, referred to as DreamLIP, demonstrate significant improvements across a range of benchmark tasks. Notably, in image-text retrieval tasks on datasets such as MSCOCO and Flickr30k, DreamLIP surpasses the performance of CLIP models trained on substantially larger datasets by notable margins. For example, DreamLIP trained on 30M image-text pairs sometimes achieves performance comparable to or exceeding that of CLIP models trained with 400M pairs. This underlines the potency of long captions in extracting and utilizing the detailed semantic richness inherent in visual data.

Furthermore, in the domain of semantic segmentation, DreamLIP's nuanced feature alignment contributes to robust performance advancements, particularly in challenging segmentation tasks which require detailed comprehension of the visual content. This reinforcement of fine-grained semantic alignment embodied in DreamLIP is further validated by improvements in additional vision-language comprehension tasks.

Theoretical and Practical Implications

The practical implications of this work are noteworthy. By establishing that long captions can effectively substitute larger datasets, DreamLIP addresses the limitations posed by data availability and quality in large-scale pre-training scenarios. This approach promises a paradigm shift where the focus can partly shift from the quantity to the quality and depth of annotations, facilitated by generative capabilities of advanced MLLMs.

Theoretically, this paper highlights the potential of leveraging exhaustive linguistic descriptors to deepen the semantic understanding and representation of visual content. It opens new avenues for studying multimodal learning where models can capitalize on more contextually enriched textual narratives to enhance their comprehension of complex visual domains.

Future Directions

The paper underscores the need for future research to delve into the nuanced interplay between visual data and its linguistic descriptors. Exploring variants of MLLMs, or possibly interactive learning frameworks that can refine caption quality and correspondences iteratively, might hold further promise. Moreover, addressing issues surrounding hallucinations in generated captions could refine these models' performance by mitigating misalignment in training datasets.

In conclusion, the innovative use of detailed long captions marks a significant step forward in language-image pre-training, fostering improved alignment between text and visual modalities. DreamLIP stands as a testament to the growing potential of leveraging sophisticated textual annotations to empower multimodal machine learning.