基本上,这些原始解决方案主要依赖于传统的AVOS技术;神经网络的学习能力尚不充分。 基于像素实例Embedding的方法:首先生成像素级实例embeddings,然后选择聚类为前景或者背景的代表性embeddings。最终,被采样embeddings的label被传播给其他embeddings。聚类和传播是无监督的。虽然使用了较少的注释,但这些方法的却是支离破碎且复杂...
Figure 3 shows t-SNE embedding of ~1,200 such representations which were sampled over a period of 2 hours with fixed network weights which had been learned after 25 hours of training. The points in the visualization were also color-coded based on the maximum Q values at the output layer ...
GIGA AI,清华,https://github.com/JeffWang987/WorldDreamer,但是尚未更新代码 将所有模态的数据都转成embedding,然后mask住做预测,再通过decoder转回去,和videopoet的思路有一些相似,都是利用LLM结构来做视频生成 VideoPoet: A Large Language Model for Zero-Shot Video Generation(2023.12.21) 作者来自于Google,论...
Learning in Educational Computer Games for Novices: The Impact of Support Provision Types on Virtual Presence, Cognitive Load, and Learning Outcomes Embedding support devices in educational computer games has been asserted to positively affect learning outcomes. However, there is only limited direct emp...
整个系统分为三部分:一是用C3D生成visual feature,二是用skip-thought或者LSTM生成一个sentence embedding,三是将这两部分的feature融合在一起 然后生成alignment score和boundary offset。alignment score代表了输入的query和clip是否匹配,boundary offset调整了 输入clip的边界。
Deep Embedding for Face Recognition in Public Video SurveillanceFace recognitionConvolutional neural networksPublic video surveillanceTriplet lossFace recognition is essential to the surveillance-based crime investigation. The recognition accuracy on benchmark datasets has been boosted by deep learning, while ...
Some methods first perform semantic segmentation followed by boundary detection [31], pixel clustering [32, 33], or learning an embedding to form instance masks [34–37]. However, these methods involve multiple stages and/or expensive clustering procedures, thus limiting their viability for real-...
1. Frame-Level Embedding 模型框架:由于需要兼容 Matching Track 对帧级别特征的需求,微信视觉团队训练的表征模型是在帧级别上进行的,主要基于 contrastive learning 框架进行自监督训练。对于采样到的视频帧,微信视觉团队基于上面提到的增强方式对视频帧进行不同的变换增强得到两张图像作为正样本,其他图像作为负样本进行...
这种基于内容理解的推荐的召回效果肯定是不如基于用户行为的召回算法,比如graph embedding ,但是它的最大的用处是用来解决推荐冷启动问题。还有在直播行业风控领域比如软色情视频识别也有非常大的需求去做视频的多模态学习。再比如广告领域 现在也看到很多公司在做内容理解。 总的来说,这个方向还是在工业界有很大的实际...
1. Frame-Level Embedding 模型框架:由于需要兼容 Matching Track 对帧级别特征的需求,微信视觉团队训练的表征模型是在帧级别上进行的,主要基于 contrastive learning 框架进行自监督训练。对于采样到的视频帧,微信视觉团队基于上面提到的增强方式对视频帧进行不同的变换增强得到两张图像作为正样本,其他图像作为负样本进行...