融合图像打包与专家自主架构，提升视频理解推理效率。
Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture
- 采用图像打包与AoE架构，优化长视频处理流程。
- 在多任务测试中表现优异，推理效率显著提升。
- 适合需要高效视频理解的AI应用开发人员。
在视频-语言预训练领域，现有模型在推理效率和多模态数据处理方面面临诸多挑战。本文提出基于长序列图像编码器的KunLunBaize-VoT-R1视频推理模型及其训练与应用方法。通过集成图像打包技术、自主专家（AoE）架构，并结合思维视频（VoT）大语言模型（经大规模强化学习训练）及多种训练技巧，有效提升了模型在视频推理任务中的效率与准确率。实验表明，该模型在多个测试中表现突出，为视频-语言理解提供了新解决方案。
原文摘要 · Abstract (English)
In the field of video-language pretraining, existing models face numerous challenges in terms of inference efficiency and multimodal data processing. This paper proposes a KunLunBaize-VoT-R1 video inference model based on a long-sequence image encoder, along with its training and application methods. By integrating image packing technology, the Autonomy-of-Experts (AoE) architecture, and combining the video of Thought (VoT), a large language model (LLM) trained with large-scale reinforcement learning, and multiple training techniques, the efficiency and accuracy of the model in video inference tasks are effectively improved. Experiments show that this model performs outstandingly in multiple tests, providing a new solution for video-language understanding.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。