This research delves into the complexities of video understanding within Large Multimodal Models (LMMs). It addresses the significant computational challenges associated with training and evaluating these models, particularly focusing on how to effectively improve video perception capabilities.
A key finding is the concept of "Scaling Consistency," where design choices proven effective for smaller models and datasets often translate to larger models. This offers significant efficiency gains in video model training.
The research explores numerous video-specific aspects affecting LMM performance. These include examining the impact of different video sampling techniques, architectural choices, and data composition on the quality of video understanding.
Based on these findings, the researchers introduce Apollo, a family of state-of-the-art LMMs designed for superior performance in video understanding. Apollo models demonstrate impressive efficiency in processing even hour-long videos.
Apollo achieves remarkable results on various video benchmarks. Its performance significantly surpasses existing models, demonstrating the effectiveness of the proposed approach to video LMM design.
The research highlights the crucial role of video encoding techniques in achieving efficient and effective video understanding. Selecting the right video encoding strategy significantly influences the performance of the LMM.
This work directly addresses the significant challenges in the field of video understanding using large multimodal models. The high computational costs of training and evaluation are mitigated through the identification of scaling consistency principles and optimal video encoding strategies.
The research lays a strong foundation for future advancements in video LMMs. Further exploration of the identified scaling consistency principles and optimized video encoding strategies promises to lead to even more efficient and powerful models for video understanding.
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