The text discusses the challenges faced by the computer vision community and highlights the development of multimodal foundation models with vision and vision-language capabilities. It explores various instructional strategies and introduces important multimodal conceptual frameworks and models such as CLIP, BEiT, CoCa, UniCL, MVP, and BEiTv2. The text also discusses T2I production, spatial controllability in T2I generation, and alignment with human intent. It emphasizes the differences between vision and language and the need for scalable laws. The researchers hope for continued development of prototypes and evaluation techniques to make large models more accessible.
The Evolution of Multimodal Foundation Models in Vision and Language
The field of computer vision faces various challenges, but recent advancements in multimodal foundation models have revolutionized the way we approach visual tasks. These models combine vision and language capabilities, making it possible to perform complex tasks without extensive data collection.
Instructional Strategies for Model Training
There are three primary instructional strategies for training these models:
- Label supervision: This strategy uses labeled examples to train the model. Large datasets like ImageNet are effective for this method.
- Language supervision: Unsupervised text signals, such as image-word pairs, are used to train models like CLIP and ALIGN.
- Image-Only Self-Supervised Learning: This technique relies solely on visuals as supervision signals, using methods like masked image modeling and contrast-based learning.
Key Multimodal Foundation Models
Several multimodal foundation models have emerged:
- CLIP (Contrastive Language-Image Pretraining): This model enables tasks like image-text retrieval and zero-shot categorization.
- BEiT (BERT in Vision): It adapts BERT’s masked image modeling technique for the visual domain.
- CoCa (Contrastive and Captioning Pretraining): This model combines contrastive learning with captioning loss for pre-training an image encoder.
- UniCL (Unified Contrastive Learning): It extends CLIP’s contrastive learning to image-label data.
- MVP (Masked Image Modeling Visual Pretraining): This method pretrains vision transformers using masked images and high-level feature objectives.
T2I Production and Image Generation
T2I generation aims to provide visuals based on textual descriptions. Models like Stable Diffusion (SD) utilize cross-attention-based fusion and diffusion-based creation to generate images. Techniques for improving spatial controllability and text-based editing are also explored.
Alignment with Human Intent
To ensure T2I models align well with human intent, alignment-focused loss and rewards are necessary. The study suggests a closed-loop integration of content comprehension and generation to improve alignment. The goal is to build unified vision models that combine understanding and generation tasks.
Challenges and Future Directions
There are inherent differences between vision and language, such as the lack of labeled visual data and the higher cost of archiving visual data. The study highlights the need for scaling laws in vision and the exploration of emergent properties in large vision models. The future lies in creating fully autonomous AI vision systems.
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