: Lead researchers or engineering teams working on implementing the open-source VideoSAVi framework are based out of technological hubs, universities, or tech parks in the Mangalore/Coastal Karnataka region.
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| Benchmark | Purpose | VideoSAVi Performance | |-----------|---------|----------------------| | TempCompass | Temporal reasoning | +0.4 ppt gain | | NeXTQA | Question answering on video | Competitive performance | | VideoMME | Multi-modal evaluation | +3.05% gain | | VideoChatGPT | Video captioning | +0.27 gain | | POPE | Hallucination reduction | +0.2 gain | | MMMU | Multimodal understanding | +0.1 ppt preserved | : Lead researchers or engineering teams working on
We’ve corrected & compiled his best work:
VideoSAVi uses InternVL2.5 as its backbone Video-LLM, and demonstration videos of this base model’s capabilities are widely available. The improvements that VideoSAVi brings—+4.2 ppt on MVBench, +3.9 ppt on PerceptionTest, and +6.8 ppt on EgoSchema—are visible in comparison videos showing the same base model before and after VideoSAVi self-training. In this blog post, we'll take a closer
This self-generated data—typically 24,000 preference pairs from 4,000 training videos—is then used for Direct Preference Optimization (DPO), allowing VideoSAVi to refine its own high-quality outputs and progressively improve its alignment with video content.