Tag2Text: Guiding Vision-Language Model via Image Tagging (2303.05657v3)

Abstract: This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-LLMs to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with an off-the-shelf detector with limited performance, our approach explicitly learns an image tagger using tags parsed from image-paired text and thus provides a strong semantic guidance to vision-LLMs. In this way, Tag2Text can utilize large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. As a result, Tag2Text demonstrates the ability of a foundational image tagging model, with superior zero-shot performance even comparable to fully supervised models. Moreover, by leveraging the tagging guidance, Tag2Text effectively enhances the performance of vision-LLMs on both generation-based and alignment-based tasks. Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance. Code, demo and pre-trained models are available at https://github.com/xinyu1205/recognize-anything.

An Overview of Tag2Text: Guiding Vision-LLMs via Image Tagging

The paper presents a novel vision-language pre-training framework, Tag2Text, which introduces an innovative method of infusing image tagging within vision-LLMs. By leveraging image tagging, Tag2Text provides enhanced guidance for learning visual-linguistic features, distinguishing it from previous models that depend on object tags with limited semantic scope.

Key Contributions and Methodology

Tag2Text deviates from traditional dependency on off-the-shelf object detectors by explicitly learning an image tagger using tags parsed from image-paired text. This methodology allows the model to utilize large-scale, annotation-free image tags aligned with image-text pairs, incorporating a broader range of categories such as scenes, attributes, and actions beyond mere objects.

Numerical evidence underscores the model's efficacy. Tag2Text demonstrates superior zero-shot image tagging capabilities, aligning closely with fully supervised models across various benchmarks including COCO and OpenImages. For instance, Tag2Text achieves a performance comparable to fully supervised models, attributed to its expansive tag recognition capabilities over 3,429 categories.

The paper outlines the incorporation of tagging guidance to enhance both generation-based and alignment-based tasks. This multi-task learning approach significantly elevates performance on benchmarks typically dominated by models pre-trained using massive datasets. The ability of Tag2Text to outperform these baselines, even with a smaller dataset and similar model sizes, attests to the strength of its architectural innovations.

Implications and Future Prospects

Practically, Tag2Text refines image captioning by enabling the generation of more comprehensive and controllable descriptions. This is particularly valuable for applications requiring nuanced content generation or retrieval where fine-grained control over output is necessary.

Theoretically, by reintroducing explicit tagging guidance and moving away from detector-based feature extraction, Tag2Text aligns with trends toward improving model efficiency and interpretability. It also opens doors for exploring further integration of structured domain knowledge into multi-modal representations.

In terms of future implications, the approach presents potential advantages for developing agile models capable of robust performance across varied and dynamic datasets. Additionally, this method might inspire subsequent innovations in applying multi-task learning settings beyond vision-language tasks, including other AI domains where context-rich, holistic modeling is essential.

Overall, Tag2Text sets a foundational framework that bridges image and text domains more effectively, contributing meaningful advancements to vision-language research while paving the way for enhanced applications in AI-driven image and text processing.