Alibaba-DAMO-Academy/

RynnEC-7B

RynnEC is a video multi-modal large language model (MLLM) specifically designed for embodied cognition tasks.

RynnEC can handle a variety of input types, including images, videos, visual prompts, and task instructions. Visual inputs are processed using a Vision Encoder equipped with an any-resolution strategy, while visual prompts are handled by a region encoder to extract fine-grained features. Textual inputs are seamlessly converted into a unified token stream through tokenization. For video segmentation tasks, a mask decoder is employed to transform the output segmentation embeddings into binary masks, ensuring precise and effective results.

Benchmark comparison across object cognition and spatial cognition. With a highly efficient 2B-parameter architecture, RynnEC-2B achieves state-of-the-art (SOTA) performance on complex spatial cognition tasks.

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