具备一系列AI能力:细分识别,物体检测,动作识别,常识推理,知识库推理... 先要根据问题,判断什么任务 图像问题与图像描述的关系 研究的难点和挑战 研究方向 数据集 COCO-QA来源MSCOCO VQA(visual question answering) 平衡数据集V1.9-->V2.0 Visual7W---Visual Genome的子集 图像问答模型 模型 基本都是VGG-Net和R...
Image question answering has gained huge popularity in recent years due to advancements in Deep Learning technologies and computer processing hardware which are able to achieve higher accuracies with faster processing capabilities. Processing image details over natural language information is one of the ...
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering,提出了一个基于注意力的VQA系统: 其中图片采用CNN编码: 问题使用LSTM编码: Stacked Attention: Classifier,其中G=[G_1, G_2, ..., G_M]是两层的全连接层: “AI论道”公众号是由中国科学院深圳先进技术研究院自然语言处理...
Image-Question-Answer Synergistic Network for Visual Dialog Dalu Guo, Chang Xu, Dacheng Tao UBTECH Sydney AI Centre, School of Computer Science, FEIT, University of Sydney, Darlington, NSW 2008, Australia {dguo8417@uni., c.xu@, dacheng.tao@}sydney.edu.au Abstract The ...
4.3. Visual Question Answering image.png 4.4. Image-Text Retrieval image.png 5. Limitation 最近的LLM可以在少量样本的情况下进行语境学习。然而,作者在BLIP - 2上的实验并没有观察到在为LLM提供语境VQA示例时VQA性能的提升。作者将语境学习能力的不足归因于作者的预训练数据集,每个样本只包含一个图像-文本对...
This paper presents a fine-tuned multimodal large model for power defect image-text question-answering, addressing challenges such as training difficulties and the lack of image-text knowledge specific to power defects. This paper utilizes the YOLOv8 to create a dataset for multimodal power defect ...
大规模语言模型(Large Language Model,LLM)无疑是时下最火热的 AI 概念,它不仅是人工智能领域近两年的研究热点,也在近期引发了全社会的广泛关注和讨论,OpenAI 的 GPT-3 和 ChatGPT 更是数次登上微博热搜。 LLM 强大的语言理解能力和知识储备,给大众留下了深刻的印象。LLM 所涌现的 in-context learning 能力,...
Document Image Retrieval in a Question Answering System for Document Images Koichi Kise1, Shota Fukushima2, and Keinosuke Matsumoto1 1 Department of Computer and Systems Sciences, Graduate School of Engineering, Osaka Prefecture University 2 Department of Computer and Systems Sciences, College of ...
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering - Yushi-Hu/tifa
参加了今年的ai challenger 的image caption比赛,最终很幸运的获得了第二名。这里小结一下。 最佳的caption利器当属微软的 Bottom-Up and Top-Down Attention for Image Captioning and Visual Question