Vision-Language Pre-training: Basics, Recent Advances, and Future Trends (2210.09263v1)

Abstract: This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.

Vision-Language Pre-training: Basics, Recent Advances, and Future Trends

The paper "Vision-Language Pre-training: Basics, Recent Advances, and Future Trends" provides an extensive survey on vision-language pre-training (VLP), a significant area of research lying at the intersection of computer vision and NLP. This field is focused on developing algorithms that capture the synergy between visual and textual information, enabling machines to learn from multimodal data effectively.

Categories of Vision-Language Pre-training

The paper categorizes VLP into three primary areas:

1. Image-Text Tasks: This includes tasks like image captioning, image-text retrieval, and visual question answering. Fusion-encoder architectures, which facilitate deep integration between image and text data through Transformer layers, are predominantly used in this category. Models like UNITER, VinVL, and ALBEF exemplify this approach, achieving state-of-the-art performance on various tasks.
2. Core Computer Vision Tasks: The paper discusses how VLP can enhance traditional computer vision tasks such as image classification and object detection. By reformulating these tasks into retrieval problems with language supervision, VLP models gain the capability to handle open-vocabulary challenges. Innovative work in this category includes models like CLIP and ALIGN, which use a dual-encoder design for effective visual representation.
3. Video-Text Tasks: The field also extends to video-text tasks, where the temporal dynamics of video data are integrated with textual descriptions. Models like VideoBERT and ClipBERT illustrate the trend of moving from pre-extracted features to end-to-end trainable architectures. This shift enables the capture of temporal relationships across video frames in conjunction with associated textual data.

Pre-training Objectives and Data

The research identifies key objectives such as Masked LLMing (MLM), Image-Text Matching (ITM), and Video-Text Contrastive Learning (VTC). These pre-training tasks are crucial for learning rich cross-modal representations. Furthermore, the paper highlights the significant role of large-scale datasets, noting the shift from academic-scale datasets to more expansive, web-crawled collections like Conceptual Captions and web-scale datasets used in models like CLIP.

Advanced Topics

Several advanced research topics are discussed:

• Big Models and Few-Shot Learning: The trend towards larger models, exemplified by research like SimVLM and Flamingo, aims to harness the benefits of scale to improve model generalization. Few-shot learning capabilities are being explored to enhance adaptability to new tasks with minimal data.
• Unified Modeling: Efforts to design architectures that seamlessly integrate image, text, and task-specific processing elements are underway. This aims to streamline various vision-language tasks under a unified framework.
• Knowledge and Robustness: Incorporating external knowledge sources into VLP models and evaluating their robustness to real-world scenarios are active areas of research, seeking to bolster the models' applicability and reliability in practical settings.

Conclusion and Future Directions

The paper asserts that VLP is poised to become increasingly central to modern AI research. Emphasizing the potential of large-scale multimodal pre-training, it invites further exploration into more efficient architectures and the development of standardized benchmarks for comprehensive evaluation. The ultimate goal is to create general-purpose foundation models that excel across a wide array of tasks, both in controlled settings and in the wild, offering robust and adaptable AI solutions. Such advancements will likely lead to significant breakthroughs in how AI systems interact with and understand the multimodal world.