Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture
This provides a new solution for video-language understanding, though it appears incremental as it builds on existing techniques like VoT and LLMs.
The paper tackled the problem of inefficient video-language pretraining by proposing Video-VoT-R1, which integrates image packing and an Autonomy-of-Experts architecture, resulting in improved efficiency and accuracy in video inference tasks.
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.