Core Viewpoint - The article highlights the significant advancements in domestic AI, particularly focusing on the Glint-MVT model developed by GeLing Deep Vision, which demonstrates superior performance in visual foundational models compared to international counterparts like CLIP and OpenCLIP [1][2]. Performance Evaluation - The linear probing technique was used to assess the pre-trained model's effectiveness, showing that the domestic visual foundational model achieved an average accuracy rate 2.3% higher than OpenCLIP and 1.1% higher than CLIP across 26 classification test sets [2]. Application Effectiveness - The Glint-MVT model excels in downstream tasks such as image understanding and segmentation, showcasing its ability to accurately segment complex images and identify objects even when partially obscured [4][8][12]. Technical Innovations - The Glint-MVT model incorporates a Margin-based pretrained Vision Transformer (MVT) and introduces the Margin Softmax loss function, which enhances the model's generalization ability by reducing data noise impact [13][26]. - The model utilizes virtual category construction by clustering large datasets, such as LAION 400M, into one million virtual categories, improving data scale efficiency [28]. Model Variants - The Glint-RefSeg model, built on Glint-MVT, achieves state-of-the-art (SOTA) performance in referring expression segmentation without the need for extensive training data [14]. - The MVT-VLM model demonstrates strong capabilities in image understanding, accurately identifying details such as the color and number on athletes' jerseys [15][16]. Broader Applications - Glint-RefSeg is also applicable in video segmentation, maintaining accuracy even with dynamic scenes, as demonstrated in a video of Bruno Mars [19][21]. - The model's versatility extends to embodied intelligence scenarios, effectively answering contextual questions about object placements [22][25]. Company Development - GeLing Deep Vision has been a pioneer in computer vision since 2013, focusing on practical applications of AI technology to address industry pain points, as exemplified by the Glint-MVT model [36][37]. - The company emphasizes a balance between technical innovation and practical application, avoiding the pursuit of mere academic metrics [38][39]. Community Engagement - GeLing Deep Vision adopts an open-source approach while maintaining a focus on innovation, aiming to foster a collaborative ecosystem that encourages community contributions [40]. - The leadership, including the director of the algorithm research institute, emphasizes the importance of youthful thinking and practical experience in driving technological advancements [41][42]. Industry Perspective - The article suggests that the development of AI technology is transitioning from general exploration to specialized applications, with companies like GeLing Deep Vision playing a crucial role in this evolution [44].
巧妙!一个传统技术让国产视觉基础模型直接上大分