X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks (2211.12402v2)

Abstract: Vision language pre-training aims to learn alignments between vision and language from a large amount of data. Most existing methods only learn image-text alignments. Some others utilize pre-trained object detectors to leverage vision language alignments at the object level. In this paper, we propose to learn multi-grained vision language alignments by a unified pre-training framework that learns multi-grained aligning and multi-grained localization simultaneously. Based on it, we present X$^2$-VLM, an all-in-one model with a flexible modular architecture, in which we further unify image-text pre-training and video-text pre-training in one model. X$^2$-VLM is able to learn unlimited visual concepts associated with diverse text descriptions. Experiment results show that X$^2$-VLM performs the best on base and large scale for both image-text and video-text tasks, making a good trade-off between performance and model scale. Moreover, we show that the modular design of X$^2$-VLM results in high transferability for it to be utilized in any language or domain. For example, by simply replacing the text encoder with XLM-R, X$^2$-VLM outperforms state-of-the-art multilingual multi-modal pre-trained models without any multilingual pre-training. The code and pre-trained models are available at https://github.com/zengyan-97/X2-VLM.

Overview of "X²-VLM: All-In-One Pre-trained Model For Vision-Language Tasks"

The paper "X²-VLM: All-In-One Pre-trained Model For Vision-Language Tasks" presents an innovative approach to vision-LLMing by introducing a unified pre-training framework designed to establish multi-grained alignments between visual and textual data representations. X²-VLM distinguishes itself from previous models by incorporating both image-text and video-text pre-training within a single, flexible modular architecture, significantly broadening its applicability across diverse vision-language tasks.

Technical Contributions

1. Multi-Grained Alignments: The authors propose a framework that simultaneously addresses multi-grained aligning and localization. In contrast to other methods that typically focus on image-text alignments or rely on object detectors, X²-VLM captures a variety of visual concepts linked to diverse text descriptions directly. This capability allows for more nuanced handling of weakly correlated visual-text pairs by learning component mapping between images and textual descriptions.
2. Flexible Modular Architecture: X²-VLM employs a modular architecture comprised of vision, text, and fusion modules, where each module is based on Transformer layers. This design not only enhances transferability across languages and domains but also allows straightforward integration with alternative text encoders, as evidenced by its successful substitution of a multilingual text encoder without additional multilingual pre-training.
3. Unified Image and Video Encoding: By unifying the encoding process for images and videos, X²-VLM can leverage large datasets more efficiently and excel in both image-text and video-text tasks. The model applies a novel method to derive all multi-grained visual concepts within an image through a single pass of the vision transformer, contributing to reduced computational complexity.

Empirical Evaluation

X²-VLM demonstrates superior performance across several image-text and video-text benchmarks, including retrieval, visual question answering (VQA), visual reasoning, and grounding tasks. Notably, in the domain of multilingual multi-modal tasks, X²-VLM outperforms current leading models that rely extensively on costly multilingual pre-training data. This success is attributed to the inherent capacity of its modular architecture which allows easy adaptation of the model's cross-modal abilities to non-English domains.

In image-text retrieval, X²-VLM shows high performance on standards like MSCOCO and Flickr30K. For instance, it outperforms BLIP, a model designed explicitly for generative tasks, on captioning performance, illustrating its competence as a versatile VLM for tasks beyond understanding.

Additionally, the flexibility of the model for multilingual tasks is validated when tested against such benchmarks without tailored multilingual pre-training, showcasing remarkable performance across several non-English languages solely by replacing the text encoder with XLM-R.

Implications and Future Directions

The proposed methodology has significant implications for both practical applications and future research directions. Practically, the modularity and adaptability of X²-VLM can substantially reduce the time and resources required to deploy effective vision-language systems in various linguistic and computational contexts. Theoretically, the framework prompts further exploration into integrating multi-grained feature alignment across different modalities, potentially leading to models with even broader understanding capabilities.

Future work might explore refining the pre-training objectives to further enhance fine-grained alignments, perhaps by introducing auxiliary tasks that emphasize relational understanding across modalities. Moreover, additional research could consider examining the scalability of this framework with increased datasets and diversified contexts to ascertain its generalization capacity.

In conclusion, X²-VLM sets a new standard in pre-trained models for vision-language tasks by simultaneously advancing the state-of-the-art in image and video understanding while maintaining efficient adaptability and scalability. The insights gained from this paper pave the way for more flexible and potent multimodal systems capable of understanding and aligning complex and varied visual and linguistic data.