2024年7月24日 · Combining self-augmentation and specialist-augmented training, VILA$^2$ consistently improves the accuracy on a wide range of benchmarks over the prior art, producing a reusable pretraining dataset that is 300x more cost-efficient than human labeling.

VILA is a family of open VLMs designed to optimize both efficiency and accuracy for efficient video understanding and multi-image understanding. [2025/1] As of January 6, 2025 VILA is now part of the new Cosmos Nemotron vision language models.

2024年11月19日 · VILA 是一种视觉语言基础 模型，它通过在预训练阶段对大型 语言模型 （LLM）进行增强，使其能够处理和理解视觉信息。 其核心思路是将图像和文本数据进行联合建模，通过控制比较和数据增强，提升模型在视觉语言任务上的 性能。 在 VILA 的基础上，还延伸出了集成视频、图像、语言理解和生成的基础模型VILA-U、支持 1024 帧长视频训练和推理的 LongVILA，以及 World Model Benchmark 等工作。 同时，在最新推出的 V I LA2 中，采用三 …

2024年7月24日 · With the combined self-augmented and specialist-augmented training, we introduce VILA 2 (VILA-augmented-VILA), a VLM family that consistently improves the accuracy on a wide range of tasks over prior art, and achieves new state-of-the-art results on MMMU leaderboard among open-sourced models.

2024年7月25日 · With the combined self-augmented and specialist-augmented training, we introduce VILA^2 (VILA-augmented-VILA), a VLM family that consistently improves the accuracy on a wide range of tasks over prior art, and achieves new state-of-the-art results on MMMU leaderboard among open-sourced models.

2024年9月19日 · With the combined self-augmented and specialist-augmented training, we introduce VILA2 (VLM-augmented-VLM), a VLM family that consistently improves the accuracy on a wide range of tasks over prior art, including MMMU leaderboard, with a reusable pretraining dataset that is 300x more cost-efficient than human labeling.

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2025年1月15日 · 预训练分为两个阶段，第一阶段是caption的训练，而第二阶段是image captioning 和 Referring Expression Comprehension (REC)两个训练目标混合。 REC是在给定对象的文本描述的情况下预测图像中边界框的任务，以 VQA 的形式进行训练，BP的时候只使用answer部分的loss。 在最后的3w个step中，将输入分辨率从224×224提升为490×490。 2、对齐. 对齐阶段分别训练CogVLM-Chat和CogVLM-Grounding两个模型。 CogVLM-Chat注重通用 …

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