Position-guided Text Prompt for Vision-Language Pre-training (2212.09737v2)

Abstract: Vision-Language Pre-Training (VLP) has shown promising capabilities to align image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we observe that VLP models often lack the visual grounding/localization capability which is critical for many downstream tasks such as visual reasoning. In this work, we propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP. Specifically, in the VLP phase, PTP divides the image into $N\times N$ blocks, and identifies the objects in each block through the widely used object detector in VLP. It then reformulates the visual grounding task into a fill-in-the-blank problem given a PTP by encouraging the model to predict the objects in the given blocks or regress the blocks of a given object, e.g. filling P" orO" in aPTP`The block P has a O". This mechanism improves the visual grounding capability of VLP models and thus helps them better handle various downstream tasks. By introducing PTP into several state-of-the-art VLP frameworks, we observe consistently significant improvements across representative cross-modal learning model architectures and several benchmarks, e.g. zero-shot Flickr30K Retrieval (+4.8 in average recall@1) for ViLT \cite{vilt} baseline, and COCO Captioning (+5.3 in CIDEr) for SOTA BLIP \cite{blip} baseline. Moreover, PTP achieves comparable results with object-detector based methods, and much faster inference speed since PTP discards its object detector for inference while the later cannot. Our code and pre-trained weight will be released at \url{https://github.com/sail-sg/ptp}.

Enhancing Visual Grounding in Vision-Language Pre-Training with Position-Guided Text Prompts

The paper presents an innovative approach to improve the visual grounding capability in Vision-Language Pre-Training (VLP) by introducing a paradigm known as Position-guided Text Prompt (PTP). The authors identify a fundamental limitation in prevalent VLP models - their lack of robust visual grounding and localization abilities, which are critical for downstream tasks such as visual reasoning and question answering. Their PTP framework aims to address this issue by embedding positional information into the pre-training phase of VLP models.

Methodological Overview

The PTP paradigm divides images into consistent $N \times N$ grids, utilizing an object detector to identify significant objects within each block. This task is then reformulated into a fill-in-the-blank format, where the model, guided by text prompts, predicts the objects in specific blocks or locates the blocks for given objects. The approach effectively integrates positional information into existing VLP architectures without altering their core structure, thereby maintaining efficiency during both training and inference.

To test their paradigm, the authors integrate PTP with leading VLP frameworks such as ViLT, CLIP, and BLIP. These integrations demonstrate the flexibility and adaptability of the PTP approach across different architectures of VLP models.

Experimental Results

Key numerical results highlight the performance improvements brought by PTP. Specific models trained with PTP show substantial gains in zero-shot learning scenarios and outperform baseline models without PTP in both image-to-text and text-to-image retrieval tasks. For instance, PTP-VILT and PTP-BLIP exhibit significant improvements across benchmarks such as Flickr30K Retrieval and COCO Captioning, with increases of 4.8% in average recall@1 for ViLT in zero-shot retrieval. Furthermore, PTP achieves comparable performance to object-detector-based methods while offering the advantage of faster inference by discarding the object detector in the evaluation phase.

The paper also explores different prompt designs and validates the robustness of PTP through a comprehensive ablation paper. The analysis confirms that incorporating positional prompts enhances the model's abilities to learn and utilize position-aware features. Moreover, visualizations illustrate that models trained with PTP can predict object positions and categories within an image more accurately.

Implications and Future Prospects

The introduction of PTP marks a critical step forward in the development of VLP models by more effectively leveraging position information. This methodology shows promise in improving the efficiency and accuracy of VLP models across a spectrum of vision-language tasks, particularly those that rely on understanding spatial relationships.

Practically, PTP's incorporation can enhance applications in domains such as autonomous vehicles, medical imaging, and interactive AI systems, where precise object localization is crucial. Theoretically, PTP enriches the VLP landscape by offering a novel perspective on integrating spatial information, which could fuel further research into multi-modal representation learning and cross-modal interaction models.

Future work may explore the scalability of PTP by applying it to larger datasets and more complex vision-language tasks. Additionally, refining the object tagging mechanism using more advanced object detectors or unsupervised approaches could further improve the grounding capabilities of VLP models.

In conclusion, the Position-guided Text Prompt framework substantially enriches the VLP paradigm by equipping models with enhanced visual grounding and localization capabilities, contributing to more sophisticated and contextually aware AI systems.