在Zero-shot Captioning 中,Qwen-VL 在 Flickr30K 数据集上取得了 SOTA 的结果,并在 Nocaps 数据集上取得了和 InstructBlip 可竞争的结果。 在General VQA 中,Qwen-VL 取得了 LVLM 模型同等量级和设定下 SOTA 的结果。 文本导向的视觉问答(Text-oriented VQA) Model typeModelTextVQADocVQAChartQAAI2DOCR-VQA...
For zero-shot image captioning, Qwen-VL achieves the SOTA on Flickr30K and competitive results on Nocaps with InstructBlip. For general VQA, Qwen-VL achieves the SOTA under the same generalist LVLM scale settings. Text-oriented VQA (Focused on text understanding capabilities in images) Model type...
chat(tokenizer, None, question, generation_config, history=None, return_history=True) print(f'User: {question}\nAssistant: {response}') question = 'Can you tell me a story?' response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True) ...
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NoCapsFlickr30KVQAv2devOK-VQAGQASciQA-Img (0-shot)VizWiz (0-shot) Generalist ModelsFlamingo-9B-61.551.844.7--28.8 Flamingo-80B-67.256.350.6--31.6 Unified-IO-XL100.0-77.954.0--- Kosmos-1-67.151.0---29.2 Kosmos-2-66.745.6--- BLIP-2 (Vicuna-13B)103.971.665.045.932.361.019.6 Instruct...
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Multi-modal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a wide range of domains. However, the la
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