This work presents video depth anything based on depth anything v2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability It can proactively update responses during a stream, such as recording activity changes or helping with the next steps in real time. Learning united visual representation by alignment before projection if you like our project, please give us a star ⭐ on github for latest update
Hack the valley ii, 2018 Unlike previous models that serve as offline mode (querying/responding to a full video), our model supports online interaction within a video stream It is designed to comprehensively assess the capabilities of mllms in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.
Added a preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the llm background section.
