Meta内讧大戏再升级！首席AI官Alexandr Wang审核图灵奖大佬论文，LeCun亲自发帖疑似暗讽28岁新上司。没有PhD、没开源代码、没发表论文，都称 不上AI研究员。 Meta内部的AI大戏，剧情又加码了！ 最近，首席科学家LeCun亲自下场，发了一篇帖子，字里行间疑似在「内涵」新BOOS——Alexandr Wang。 这是一段写给AI领域的记者和评论家们的话——在AI领域工作的人，并非都是「研究员」。 他特意列出了，「研究员」的学术标准—— 发表研究成果、开源代码：查阅个人Google Scholar或GitHub。 发表论文和开源代码，对学术圈有实打实影响：查看Google Scholar论文引用量和h指数。 拥有AI相关领域PhD学位。当然，没有博士学位的也有优秀的研究员，但也凤毛麟角。 从读研期间就发表过论文，工作后也持续输出，不然就转型成了工程师或管理者。 LeCun话锋一转，直言「研究与工程/产品开发」是两码事，因其动机、激励机制和运作模式都各不相同。 有些人两者兼能，有些人则只能做其中一种。 简单来说，衡量一个研究员要看其学术影响力，而衡量一个工程师要看其产品影响力。 对于推动科学 ...

Core Viewpoint - The article discusses the advancements in computer vision, particularly focusing on the development and capabilities of the DINO series of models, emphasizing the transition from supervised to self-supervised learning paradigms in AI [2][15][29]. Group 1: DINO Model Evolution - DINO, DINOv2, and DINOv3 represent significant milestones in self-supervised learning, with DINOv3 achieving state-of-the-art performance across various tasks without the need for labeled data [2][15][31]. - DINOv3 has expanded its training dataset to 1.7 billion images and model parameters to 7 billion, significantly enhancing its capabilities compared to its predecessors [9][31][36]. - The introduction of innovative techniques in DINOv3, such as Gram Anchoring and RoPE, has improved the model's ability to generate high-resolution dense features, addressing limitations seen in DINOv2 [18][24][28]. Group 2: Performance Metrics - DINOv3 outperforms previous models in multiple benchmarks, achieving a segmentation score of 55.9, depth estimation of 0.309, and video tracking accuracy of 83.3, showcasing its superior performance in dense prediction tasks [17][31]. - The model's performance in image classification tasks is also notable, with an accuracy of 90.4 on ImageNet ReaL, indicating its robustness across various applications [17][31]. Group 3: Practical Applications - DINOv3 is being utilized in real-world applications, such as analyzing satellite images for environmental monitoring and supporting climate finance processes, demonstrating its practical impact [39][40]. - The model's ability to operate effectively without fine-tuning makes it suitable for edge applications where multiple visual prediction tasks need to be executed simultaneously [34][36]. Group 4: Community Engagement and Accessibility - Meta has open-sourced DINOv3, providing a complete backbone network and evaluation heads for community use, facilitating further research and development [13][36]. - The model family includes various distilled versions to cater to different computational needs, ensuring accessibility for researchers and developers [36][37].

Core Viewpoint - The article discusses the introduction of SimDINO and SimDINOv2, two new visual pre-training models developed by a collaboration of researchers from various institutions, which simplify the training process of the existing DINO and DINOv2 models while enhancing their performance [1][5][12]. Group 1: Model Development - SimDINO and SimDINOv2 are designed to address the complexities associated with DINO and DINOv2, which are currently leading models in visual pre-training [2][4]. - The new models utilize coding rate regularization to simplify the training process and improve robustness and performance [12][16]. - The core idea is to remove complex empirical design components from the original DINO and DINOv2 training processes, making the models easier to train and implement [12][18]. Group 2: Methodology - The introduction of coding rate regularization helps prevent representation collapse, which was a significant issue in the original models [14][17]. - SimDINO retains the EMA self-distillation scheme and multi-view data augmentation from DINO but modifies the contrastive learning approach to use Euclidean distance or cosine similarity instead of high-dimensional projections [18][19]. - SimDINOv2 further simplifies the iBOT mechanism introduced in DINOv2, enhancing the model's efficiency [19]. Group 3: Experimental Validation - Extensive experiments on various datasets, including ImageNet-1K, COCO val2017, and ADE20K, demonstrate that SimDINO and SimDINOv2 outperform the DINO series in terms of computational efficiency, training stability, and downstream task performance [22][23]. - In specific evaluations, SimDINO achieved a linear segmentation mIoU of 33.7 and mAcc of 42.8, while SimDINOv2 reached mIoU of 36.9 and mAcc of 46.5, showcasing significant improvements over DINO and DINOv2 [30]. Group 4: Theoretical Insights - The research team proposes a theoretical framework for selecting hyperparameters in SimDINO, focusing on balancing the gradients of the coding rate regularization term and the distance term [33][34]. - This theoretical analysis provides a clearer optimization target and reduces the complexity of hyperparameter tuning, making the training process more straightforward [39]. Group 5: Future Directions - The research team suggests potential improvements for SimDINO, including exploring self-supervised objectives that do not require self-distillation optimization [43].