Use VideoMAE Kinetics for Free

VideoMAE Kinetics is an AI model released 2022 by MCG-NJU, a leading organization in large language model research. VideoMAE fine-tuned on Kinetics-400 — accurate human-action recognition across 400 daily-life and sports activities. With a large number of parameters and a a large context window context window, VideoMAE Kinetics is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.

VideoMAE Kinetics uses a dense transformer architecture with a large number of parameters of parameters, all of which are active for every input token. Dense architectures provide predictable, consistent behavior across diverse task types — there are no "cold" experts to warm up, and quality is uniform across prompts. MCG-NJU's training pipeline combined large-scale pretraining on a large-scale multilingual pretraining dataset with supervised fine-tuning and alignment techniques (RLHF or DPO), producing VideoMAE Kinetics's characteristic balance of capability, helpfulness, and safety.

As of 2026-10-16, VideoMAE Kinetics by MCG-NJU demonstrates measurable performance on standard AI evaluation benchmarks. On Kinetics-400 Top-1 — which measures model performance on specialized evaluation tasks — it achieves 79.9%. These scores place VideoMAE Kinetics in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.

The primary strengths of VideoMAE Kinetics are: Fine-tuned VideoMAE for Kinetics action recognition; CC-BY-NC 4.0. These make it particularly well-suited for Video action recognition fine-tuning baselines. Users working on complex, multi-step problems benefit most from VideoMAE Kinetics's ability to maintain coherence across long inputs and produce structured, verifiable outputs.

Like all current AI systems, VideoMAE Kinetics has limitations: Research model; Classification only. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. MCG-NJU continues to improve VideoMAE Kinetics through ongoing research.

VideoMAE Kinetics is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making VideoMAE Kinetics accessible to students, researchers, developers, and early-stage teams globally.

As of 2026-10-16, VideoMAE Kinetics by MCG-NJU demonstrates measurable performance on standard AI evaluation benchmarks. On Kinetics-400 Top-1 — which measures model performance on specialized evaluation tasks — it achieves 79.9%. These scores place VideoMAE Kinetics in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.

VideoMAE base fine-tuned on Kinetics-400

The training pipeline for VideoMAE Kinetics involves multiple stages: large-scale pretraining on diverse multilingual text and code corpora to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. MCG-NJU's research team — based in China — has optimized VideoMAE Kinetics for the specific capability profile described above.

Inference efficiency: VideoMAE Kinetics is designed to deliver quality outputs at practical serving speeds. Its context capacity supports complex multi-turn conversations without quality degradation.

Getting started with VideoMAE Kinetics on LITHOVEX AI takes under two minutes. Follow these steps:

For best results with VideoMAE Kinetics: provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions.

MCG-NJU is a leading organization in AI research and large language model development. Their team has built expertise across natural language processing, model architecture design, and alignment research. MCG-NJU's models are used by researchers, developers, and enterprises worldwide. Their commitment to advancing AI capability while maintaining responsible deployment practices has made them a respected contributor to the global AI ecosystem. The organization continues to publish research and release models that push the boundaries of what AI systems can achieve.

Founded: 2021, headquartered in China.

Visit the official MCG-NJU website →

VideoMAE Kinetics by MCG-NJU competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.

Both VideoMAE Kinetics and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.

VideoMAE Kinetics is released under CC BY-NC 4.0, which means you can download and run it on your own hardware. Below are the quickest ways to get started.

The Kinetics-fine-tuned VideoMAE serves global applications requiring ready-to-use action recognition — from smart gym equipment in the US to factory safety monitoring in China to patient monitoring systems in hospitals worldwide.

VideoMAE Kinetics by MCG-NJU has the following version history and notable updates:

For the complete changelog and latest release notes, visit the official MCG-NJU model page.