Paper: https://arxiv.org/pdf/2411.14402)
Github Link: https://github.com/apple/ml-aim)
This research introduces AIMV2, a family of large-scale vision encoders pre-trained using a novel multimodal autoregressive method. Unlike previous contrastive methods, AIMV2 simultaneously predicts image patches and text tokens, offering scalability and simplicity. The resulting models demonstrate strong performance across various downstream tasks, including image recognition, object detection, and multimodal understanding, often outperforming state-of-the-art alternatives. Extensive experiments explore AIMV2's scaling properties and the impact of design choices, showing its robustness and versatility. The work concludes that AIMV2's unified objective function enables efficient training and superior performance.
ai , computer vision , cv , apple , artificial intelligence , arxiv , research , paper , publication
