Key Points
1. The development of "foundational models" has gained significant traction due to their ability to be trained on a large-scale broad data and then adapted for a wide range of downstream tasks related to the original trained model.
2. Recent large language models (LLMs) have played a significant role in the surge of foundational models due to their ability to be utilized for zero/few-shot learning, achieving impressive performance without requiring large-scale task-specific data or model parameter updating.
3. Pre-trained vision-language models (VL) have demonstrated promising zero-shot performance on different downstream vision tasks, including image classification and object detection.
4. Several research efforts have been devoted to developing large foundation models that visual inputs can prompt, such as performing class-agnostic segmentation given an image and a visual prompt.
5. Different architecture designs, contrastive learning objectives, and multi-modal datasets have been utilized for training foundational models in computer vision.
6. These models have demonstrated capabilities in language understanding, generation, reasoning, and code-related tasks, covering a wide range of applications systematically and comprehensively.
7. Various new methodologies have been proposed to equip large language models (LLMs) with the ability to understand and reason about the visual modality, utilizing contrastive learning, generative objectives, and training with a general generative objective.
8. Efforts have been made to train foundational models for generic vision-language learning, utilizing pre-training on generative and contrastive tasks to enhance generalizability across vision-language tasks.
9. The performance of foundational models has been extensively studied and compared with other methods, showcasing their generalizability and applicability for a wide range of downstream vision-language tasks.
Summary
The research paper discussed in this summary provides a comprehensive review of foundational models in computer vision, focusing on large-scale language models (LLMs) and pre-trained vision-language models (VLs). The paper outlines the significance of vision systems that understand visual scenes and the need for models to bridge modalities such as vision, text, audio, and depth. The models, referred to as foundational models, demonstrate contextual reasoning, generalization, and prompt capabilities without retraining.
The paper categorizes the existing foundational models into different classes based on their inputs, outputs, and utilization. These include textually prompted models, visually prompted models, and those based on heterogeneous modalities. It discusses various architecture designs, training objectives, and pre-training datasets used for these models. It further analyzes and categorizes recent developments in the field, covering a wide range of applications of foundation models systematically and comprehensively.
The review discusses various approaches to equipping LLMs with visual modality, including in-context learning with multimodal inputs, using LLMs as a general interface for other modalities, pre-training of vision-language models, and hybrid contrastive and generative learning. The paper also explores the performance of different pre-training methods and compares captioning-based models with CLIP-style models for vision-language pre-training. Additionally, the paper presents methods for foundational models for generic vision-language learning, such as UNITER, which emphasizes generative and contrastive pre-training tasks.
In summary, the paper provides a comprehensive overview of foundational models in computer vision, outlining different approaches and methods for integrating vision and language modalities in pre-training models. It covers a wide range of applications and discusses the significance of these models for various vision-language tasks.
The paper discusses several foundational models that aim to align different modalities, such as image, video, audio, and text, to learn meaningful representations. The models discussed include CLIP2Video, AudioCLIP, ImageBind, and MACAW-LLM. These models extend the capabilities of the CLIP model to handle videos, audio, and multiple paired modalities. Additionally, the paper introduces models like Painter and Valley, which aim to perform different tasks simultaneously and adapt to new tasks with minimal prompts and examples. It also addresses the challenges of incorporating temporal consistency and context for video-language understanding.
Reference: https://arxiv.org/abs/2307.13721v1