Image Captioner Using CLIPxGPT is Image Captioning Model based on OpenAI's CLIP and GPT-2. The Model uses a Mapping module to "translate" CLIP embeddings to GPT-2. The model is trained on the Flickr30k dataset, downloaded from Kaggle The goal of the project was to find out about...
apiwebsiteimageaiimage-captioningimage-captionhcaptchaimage-caption-generatorhcaptcha-solver UpdatedAug 20, 2023 Python nithintata/image-caption-generator-using-deep-learning Star10 Automatically generates captions for an image using Image processing and NLP. Model was trained on Flickr30K dataset. ...
K. Tran, X. He, L. Zhang, J. Sun, Rich image captioning inthe wild, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016,pp. 434–441. K. Fu, J. Jin, R. Cui, F. Sha, C. Zhang, Aligning where to see and what to tell: Image captioning withregion-basedattention...
Two-stage learning strategy: learning the term generator network using only a standard image caption dataset(MSCOCO), learning the language generator network on styled text data(romantic novels) "Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention (ECCV 2018) Style...
我们还将在 COCO 上经过微调的模型直接移植到 Flickr30K 上进行zero-shot检索,结果如表 6 所示,BLIP 也以较大优势超过了现有方法。 5.2. Image Captioning(图像字幕) BLIP在字幕生成中与其他VLP相比的结果: 我们考虑了两个图像标题数据集:NoCaps和 COCO,这两个数据集均使用在 COCO 上经过微调的模型和 LM 损失...
- METEOR,也是常见的机器翻译系统的评估指标,其通过建立一个短语词表(phrase table),考虑了输出文本是否使用了相似短语。- CIDEr,考虑了句子中的文字与图片的相关性- ROUGE-L,是text summerization的评估指标常见的image captioning 系统的标准测试数据集包括:- Flickr 8k- Flickr 30k- MS COCO...
The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains linking mentions of the same entities in images, as well as 276k manually annotated boundi...
Flickr30K: The dataset contains 31,000 images with 5 captions each. Disk space required: 4 GB.UsageTo train the model and download the datasets if they are not downloaded yet, you must run the following command:python -m src.train Once...
该任务涉及到了图像与自然语言两个模态,然而图像空间与自然语言空间本就十分庞大,并且两者之间存在巨大的语义鸿沟。 如何将两个庞大的语义空间进行对齐,这是该任务的重点。本项目对ClipCap: CLIP Prefix for Image Captioning论文进行介绍,并且对论文在Flickr30k中文数据集上进行实验复现和效果展示。
- CIDEr,考虑了句子中的文字与图片的相关性- ROUGE-L,是text summerization的评估指标常见的image captioning系统的标准测试数据集包括:- Flickr 8k- Flickr 30k- MS COCO 模型描述 常见的image captioning系统是由一个CNN+RNN的编码解码模型完成,类比一下machine translation系统,通常由一个RNN encoder + RNN ...