Video Captions Mac版是一款Fcpx视频字幕生成工具！该应用程序使用先进的Al SpeechRecognition技术将您的Final Cut Pro项目音频转录为可动画化的标题。...Captions for Mac(Fcpx字幕生成工具) 图片Video Captions Mac版功能特色 连接 Final Cut Pro 以接收为您的项目导出的音频- 自动转录支持多种语言的音频-
在这个数据集上，共有物体检测 (Detection)、人体关键点检测 (Keypoints)、图像分割 (Stuff)、图像描述生成 (Captions) 四个类别的比赛任务。...其中图像描述生成任务 (Captions)，需要同时对图像与文本进行深度的理解与分析，相比其他三个任务更具有挑战性，因此也吸引了更多的工业界（Google，IBM，Microsoft）以及国际顶尖院校（...经过充分的训练，腾讯 AI Lab 研发的图像描述生成模型在微软 MS COCO 的 Captions 任务上排名第一，超过了微软、谷歌、IBM 等科技公司。 [1]. O. Vinyals, A....Zweig,「From Captions to Visual Concepts and Back」, CVPR 2015. [6]. K. Xu, J. Ba, R. Kiros, K.
在这个数据集上，共有物体检测 (Detection)、人体关键点检测 (Keypoints)、图像分割 (Stuff)、图像描述生成 (Captions) 四个类别的比赛任务。...其中图像描述生成任务 (Captions)，需要同时对图像与文本进行深度的理解与分析，相比其他三个任务更具有挑战性，因此也吸引了更多的工业界（Google，IBM，Microsoft）以及国际顶尖院校（...最终，通过充分的训练，腾讯 AI Lab 研发的图像描述生成模型在微软 MS COCO 的 Captions 任务上排名第一，超过了微软、谷歌、IBM 等科技公司。 ? [1]. O....Zweig,「From Captions to Visual Concepts and Back」, CVPR 2015. [6]. K. Xu, J. Ba, R. Kiros, K.
= captions[sorted_cap_indices].squeeze() captions = Variable(captions).cuda() sorted_cap_lens...= captions[sorted_cap_indices].squeeze() captions = Variable(captions).cuda() sorted_cap_lens...captions = train_captions + test_captions for sent in captions: for word in sent:...(rev) test_captions_new = [] for t in test_captions: rev = [] for...= self.load_captions(data_dir, train_names) test_captions = self.load_captions(data_dir,
1.Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning（通过比较级对抗学习产生多样而准确的视觉描述...The problem is both fundamental and interesting, as most machine-generated captions, despite phenomenal...by comparing a set of captions within the image-caption joint space....By contrasting with human-written captions and image-mismatched captions, the caption generator effectively...Despite many efforts, generating discriminative captions for images remains non-trivial.
代码如下： # loading captions from captions file import pandas as pd # loading captions.txt captions...it print(captions.shape) captions = captions.dropna() print(captions.shape) # training and testing...captions[captions['image'] == image] list_of_captions = tempDf['caption'].tolist() train_image_captions...['caption'].tolist() test_image_captions[image] = list_of_captions all_captions.append(list_of_captions...list all_captions = [caption for list_of_captions in all_captions for caption in list_of_captions]
"education.png", "partners.png", "support.png" }; final static String[] captions...ImageView(); final ScrollPane list = new ScrollPane(); final Hyperlink[] hpls = new Hyperlink[captions.length...; i++) { final Hyperlink hpl = hpls[i] = new Hyperlink(captions[i]); final Image..."education.png", "partners.png", "support.png" }; final static String[] captions...; i++) { final Hyperlink hpl = hpls[i] = new Hyperlink(captions[i]); final
LSTM, Concatenate from tensorflow.keras.models import Model # 数据集路径 IMAGES_DIR = "path/to/images" CAPTIONS_FILE...= "path/to/captions.txt" # 读取图像与描述数据 def load_data(): captions = {} with open(CAPTIONS_FILE...(image_id, []).append(caption) return captions captions_dict = load_data() 图像特征提取 我们将使用预训练的InceptionV3...# 构建文本标记器 all_captions = [caption for captions in captions_dict.values() for caption in captions] tokenizer...= Tokenizer() tokenizer.fit_on_texts(all_captions) # 文本转序列 def text_to_sequence(text): sequence
表2：多模态方法在UCM-captions数据集上的结果。 ? 表3：多模态方法在Sydney-captions数据集上的结果。 ? 表4：多模态方法在RSICD数据集上的结果。 ?...图5：（a）在UCM-captions数据集上的使用RNN的多模态方法的结果。（b）在Sydney-captions数据集上使用RNN的多模态方法的度量。...（d）在UCM-captions数据集上的使用LSTM的多模态方法的结果。（e）在Sydney-captions数据集上使用LSTM的多模态方法的度量。...表7：在UCM-captions数据集上使用CNNs的基于注意力方法的结果。 ? 表8：在Sydney-captions数据集上使用CNNs的基于注意力方法的结果。 ?...表11：对UCM-captions数据集的主观评价结果。 ? 表12：对Sydney-captions数据集的主观评价结果。 ? 表13：对RSICD数据集的主观评价结果。
The problem is both fundamental and interesting, as most machine-generated captions, despite phenomenal...by comparing a set of captions within the image-caption joint space....By contrasting with human-written captions and image-mismatched captions, the caption generator effectively...exploits the inherent characteristics of human languages, and generates more discriminative captions...We show that our proposed network is capable of producing accurate and diverse captions across images
a linear combination of SPICE and CIDEr (a combination we call SPIDEr): the SPICE score ensures our captions...are semantically faithful to the image, while CIDEr score ensures our captions are syntactically fluent...that are strongly preferred by human raters compared to captions generated by the same model but trained...Discriminability objective for training descriptive captions（训练描述性标题的判别性目标） ---- 作者：Ruotian Luo,Brian...Remarkably, our approach leads to improvement in other aspects of generated captions, reflected by a
(caption) # shuffling the captions and image_names together# setting a random state train_captions, img_name_vector...random_state=1) # selecting the first 30000 captions from the shuffled set num_examples = 30000 train_captions...= train_captions[:num_examples] img_name_vector = img_name_vector[:num_examples] len(train_captions)..., len(all_captions) 使用InceptionV3来预处理图像 接下来，我们将使用InceptionV3（在Imagenet上预训练过的）对每个图像进行分类。...@[\]^_`{|}~ ') tokenizer.fit_on_texts(train_captions) train_seqs = tokenizer.texts_to_sequences(train_captions
最近，谷歌在人机交互顶级会议ACM CHI（Conference on Human Factors in Computing Systems）上展示了一个系统Visual Captions，介绍了远程会议中的一个全新视觉解决方案...论文链接：https://research.google/pubs/pub52074/ 代码链接：https://github.com/google/archat Visual Captions系统基于一个微调后的大型语言模型...在用户调研中，研究人员邀请了实验室内的26位参与者，与实验室外的10位参与者对系统进行评估，超过80%的用户基本都认同Video Captions可以在各种场景下能提供有用、有意义的视觉推荐，并可以提升交流体验...在系统工作流程中，Video Captions可以自动捕获用户的语音、检索最后的句子、每隔100毫秒将数据输入到视觉意图预测模型中、检索相关视觉效果，然后提供推荐的视觉效果。...Visual Captions的系统工作流 Visual Captions在推荐视觉效果时提供三个级别的可选主动性： 自动显示（高主动性）：系统自主搜索并向所有会议参与者公开显示视觉效果，无需用户交互。
(2) 用词嵌入层将captions_in中词的索引转换成词响亮，得到一个维度为(N, T, W)的数组。...def loss(self, features, captions): # 这里将captions分成了两个部分，captions_in是除了最后一个词外的所有词，是输入到RNN/LSTM的输入...；captions_out是除了第一个词外的所有词，是RNN/LSTM期望得到的输出。...captions_in = captions[:, :-1] captions_out = captions[:, 1:] # You'll need this mask = (...captions_out !
session = HTMLSession() request = session.get(course_url) data_video_url = '' data_captions_url...('.devsite-vplus', first=True) # data_video_url = video_info.attrs['data-video-url'] # data_captions_url...= video_info.attrs['data-captions-url'] next_url_info = request.html.find('div.devsite-steps-next...continue data_video_url 为mp4视频相对地址 data_captions_url 为字幕相对地址 通过 base_url 可得到绝对地址，后面再写吧。
虽然 OpenAI 表示，他们的训练数据集尚不会公开，但他们透露，数据集中包括 Google 发表的 Conceptual Captions 数据集。...大型图文对数据集 mini 替代版 Conceptual Captions 数据集，由谷歌在 ACL 2018 发表的论文《Conceptual Captions: A Cleaned, Hypernymed...首先，团队提出了一个新的图像标题注释数据集——Conceptual Captions，它包含的图像比 MS-COCO 数据集多一个数量级，共包括约 330 万图像和描述对。...Conceptual Captions pipeline 过滤步骤示例和最终输出 一：基于图像的过滤 算法会根据编码格式、大小、纵横比和令人反感的内容过滤图像。...不如先从 Conceptual Captions 数据集开始吧！ 访问 https://hyper.ai/datasets 或点击阅读原文，还可获取更多数据集哦！
-- Captions are optional --> <track kind="<em>captions</em>" label="English <em>captions</em>" src="/path/to/<em>captions</em>.vtt
and the image name in vectors all_captions = [] all_img_name_vector = [] for annot in annotations['...(caption) # shuffling the captions and image_names together # setting a random state train_captions,...the shuffled set num_examples = 30000 train_captions = train_captions[:num_examples] img_name_vector...= img_name_vector[:num_examples] len(train_captions), len(all_captions) Inceptions v3 图像预处理 这个步骤中需要使用...@[\]^_`{|}~ ') tokenizer.fit_on_texts(train_captions) train_seqs = tokenizer.texts_to_sequences(train_captions
= os.path.join(dataDir, 'annotations/captions_{}.json'.format(dataType)) coco_caps = COCO(captions_annFile...该批次中的预处理图像和标注存储在images和captions中。...('images:', images) # print('captions:', captions) ?...(from Step 1) to GPU if CUDA is available captions = captions.to(device) # Pass the encoder output...and captions through the decoder. outputs = decoder(features, captions) print('type(outputs):', type
文字condition 视频 Attentive Semantic Video Generation using Captions Tensorflow implementation for the paper...Attentive Semantic Video Generation using Captions by Tanya Marwah*, Gaurav Mittal* and Vineeth N....Proposed network architecture for attentive semantic video generation with captions.
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