Qwen-VL
Qwen-VL is a visual-language large model developed by Alibaba and part of the multimodal branch of the Qwen (Tongyi Qianwen) series. The model can simultaneously understand image and text inputs, supporting tasks such as visual question answering, visual reasoning, image captioning, document understanding, and GUI interaction. It has strong bilingual (Chinese and English) visual comprehension capabilities and can be applied to intelligent assistants and automated agent scenarios.
Core Facts
GPT-4V、Claude 3、Gemini是现代多模态大模型的代表，能够同时处理文本、图像等多种数据类型
Qwen-VL is a vision-language model series launched by Alibaba DAMO Academy, extended from the Qwen large language model, adopting a combined architecture of a ViT visual encoder and the Qwen language model, and achieving cross-modal alignment through a position-aware visual-language adapter.
VLMs (Vision Language Models) typically consist of a visual encoder (such as ViT), a language model, and a projection layer connecting the two, with representative models including GPT-4V/4o, Claude 3.5, the LLaVA series, and InternVL
Timeline (last 90 days)
Multimodal large language models (MLLMs) refer to large-scale models capable of processing multiple modal inputs such as text, images, and video simultaneously, such as GPT-4V and Qwen-VL.
Multimodal large language models (MLLMs) refer to large-scale models capable of simultaneously processing multiple modality inputs such as text, images, audio, and video and generating text output, with representative works including GPT-4o, Gemini, Qwen-VL, etc.
Qwen系列的多分支包括针对代码场景继续预训练的CodeQwen以及融合视觉编码器的多模态版本Qwen-VL
The specific benchmark results of MiMo-V2.6 and its horizontal comparison with similar open-source omnimodal models (such as the Qwen-VL series) remain to be tested.
Alibaba previously released the Qwen-Audio and Qwen-VL series of models.
A VLM is a multimodal model that integrates a visual encoder on top of a traditional LLM, with typical representatives including LLaVA, Qwen-VL, and InternVL.
The context length of mainstream open-source multimodal models is typically between 4K and 32K tokens: LLaVA-1.5 is 4,096 tokens, Qwen-VL is 8,192 tokens, and InternVL2 extends to 32K.
Mainstream open-source models focused on document understanding, such as InternVL2 and Qwen-VL, mostly have parameter counts in the range of 3B to 72B.
代表性的VLM包括OpenAI的GPT-4o、Google的Gemini系列以及阿里巴巴的Qwen-VL系列
Qwen-VL is a vision-language model series launched by Alibaba DAMO Academy, extended from the Qwen large language model, adopting a combined architecture of a ViT visual encoder and the Qwen language model, and achieving cross-modal alignment through a position-aware visual-language adapter.
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All Facts (19)
GPT-4V、Claude 3、Gemini是现代多模态大模型的代表，能够同时处理文本、图像等多种数据类型
90%VerifiedQwen-VL is a vision-language model series launched by Alibaba DAMO Academy, extended from the Qwen large language model, adopting a combined architecture of a ViT visual encoder and the Qwen language model, and achieving cross-modal alignment through a position-aware visual-language adapter.
85%VerifiedVLMs (Vision Language Models) typically consist of a visual encoder (such as ViT), a language model, and a projection layer connecting the two, with representative models including GPT-4V/4o, Claude 3.5, the LLaVA series, and InternVL
80%VerifiedThe Qwen Vision model (Qwen-VL series) is a multimodal large model launched by Alibaba Cloud's Tongyi team, capable of simultaneously understanding text and image inputs.
75%VerifiedThe Qwen-VL series models adopt a dynamic resolution input mechanism, enabling them to process images of different sizes without forced scaling.
70%VerifiedOpen-source multimodal models LLaVA, InternVL, and Qwen-VL typically adopt a 'vision encoder + projection layer + language model' architecture.
70%VerifiedText models supported by the Transformers framework include GPT, BERT, LLaMA, and T5; vision models include ViT, CLIP, and Stable Diffusion; audio models include Whisper and Wav2Vec; and multimodal models include LLaVA and Qwen-VL.
65%UnverifiedQwen-VL和InternVL分别来自阿里和上海AI Lab，代表国内多模态领域的前沿水平
90%Unverified阿里的Qwen-VL和智谱的CogVLM/GLM-4V是中文领域代表性的多模态模型
90%Unverified开源多模态模型包括LLaVA、MiniCPM-V、InternVL、Qwen-VL等
60%UnverifiedMultimodal large language models (MLLMs) refer to large-scale models capable of processing multiple modal inputs such as text, images, and video simultaneously, such as GPT-4V and Qwen-VL.
50%UnverifiedMultimodal large language models (MLLMs) refer to large-scale models capable of simultaneously processing multiple modality inputs such as text, images, audio, and video and generating text output, with representative works including GPT-4o, Gemini, Qwen-VL, etc.
50%UnverifiedQwen系列的多分支包括针对代码场景继续预训练的CodeQwen以及融合视觉编码器的多模态版本Qwen-VL
50%UnverifiedThe specific benchmark results of MiMo-V2.6 and its horizontal comparison with similar open-source omnimodal models (such as the Qwen-VL series) remain to be tested.
50%UnverifiedAlibaba previously released the Qwen-Audio and Qwen-VL series of models.
50%UnverifiedA VLM is a multimodal model that integrates a visual encoder on top of a traditional LLM, with typical representatives including LLaVA, Qwen-VL, and InternVL.
50%UnverifiedThe context length of mainstream open-source multimodal models is typically between 4K and 32K tokens: LLaVA-1.5 is 4,096 tokens, Qwen-VL is 8,192 tokens, and InternVL2 extends to 32K.
50%UnverifiedMainstream open-source models focused on document understanding, such as InternVL2 and Qwen-VL, mostly have parameter counts in the range of 3B to 72B.
50%Unverified代表性的VLM包括OpenAI的GPT-4o、Google的Gemini系列以及阿里巴巴的Qwen-VL系列
50%