This paper introduces a novel approach called "vision superalignment," focusing on the concept of weak-to-strong generalization for vision foundation models. The core idea is to use a weaker model to supervise and improve a stronger model, enabling the latter to achieve better performance than it would on its own. The authors propose an adaptive confidence loss function that dynamically adjusts based on the confidence level of the weak model's predictions, allowing the strong model to learn more effectively from the weak model's guidance. The study explores various scenarios, including few-shot learning, transfer learning, noisy label learning, and traditional knowledge distillation. The results show that the proposed method outperforms existing techniques in these tasks, demonstrating the effectiveness of weak-to-strong generalization in enhancing the performance of vision foundation models. The method is evaluated on multiple benchmark datasets, including CIFAR-100, ImageNet, and iNaturalist, and shows significant improvements in classification accuracy and transfer learning performance. The paper also discusses the importance of adaptive confidence distillation in overcoming the limitations of weak models and the inaccuracies of strong models' self-generated labels. By dynamically adjusting the weight given to the weak model's guidance, the strong model can better utilize its own capabilities to refine its predictions. This approach is particularly effective in scenarios where ground truth labels are not available, as it allows the strong model to learn from the weak model's predictions without relying on accurate labels. The study contributes to the broader field of superalignment by demonstrating the feasibility and effectiveness of weak-to-strong generalization in vision tasks. The results highlight the potential of this approach in advancing the capabilities of artificial intelligence in the visual domain, emphasizing the importance of nuanced supervision mechanisms in achieving superhuman performance in vision tasks. The proposed method provides a more refined mechanism for facilitating weak-to-strong knowledge transfer, offering a significant step forward in the pursuit of more sophisticated, efficient, and capable AI systems.This paper introduces a novel approach called "vision superalignment," focusing on the concept of weak-to-strong generalization for vision foundation models. The core idea is to use a weaker model to supervise and improve a stronger model, enabling the latter to achieve better performance than it would on its own. The authors propose an adaptive confidence loss function that dynamically adjusts based on the confidence level of the weak model's predictions, allowing the strong model to learn more effectively from the weak model's guidance. The study explores various scenarios, including few-shot learning, transfer learning, noisy label learning, and traditional knowledge distillation. The results show that the proposed method outperforms existing techniques in these tasks, demonstrating the effectiveness of weak-to-strong generalization in enhancing the performance of vision foundation models. The method is evaluated on multiple benchmark datasets, including CIFAR-100, ImageNet, and iNaturalist, and shows significant improvements in classification accuracy and transfer learning performance. The paper also discusses the importance of adaptive confidence distillation in overcoming the limitations of weak models and the inaccuracies of strong models' self-generated labels. By dynamically adjusting the weight given to the weak model's guidance, the strong model can better utilize its own capabilities to refine its predictions. This approach is particularly effective in scenarios where ground truth labels are not available, as it allows the strong model to learn from the weak model's predictions without relying on accurate labels. The study contributes to the broader field of superalignment by demonstrating the feasibility and effectiveness of weak-to-strong generalization in vision tasks. The results highlight the potential of this approach in advancing the capabilities of artificial intelligence in the visual domain, emphasizing the importance of nuanced supervision mechanisms in achieving superhuman performance in vision tasks. The proposed method provides a more refined mechanism for facilitating weak-to-strong knowledge transfer, offering a significant step forward in the pursuit of more sophisticated, efficient, and capable AI systems.