Video Classification with Transformers (keras.io) https://github.com/google-research/scenic/tree/main/scenic/projects/vivit 另外一个简易版本的keras实现:Video Vision Transformer (keras.io),这个实现内没有采用VIVIT提出的几种模式,直接用3D卷积提取了特征,然后跟了几层transformer。 模型从时间或者空间的dimens...
pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(Audio)、计算机视觉(Computer vision)、自然语言处理(NLP)、多模态(Multimodal)等4大类,28小类任务(tasks)。共计覆盖32万个模型 今天介绍CV计算机视觉的第六篇:视频分类(video-classification),在huggingface库内有110...
之后还有许多针对 3D 卷积网络的改进如:non-local,可分离卷积等(Video Classificationwith Channel-Separated Convolutional Networks)。基于卷积的方法虽然已经占据主流地位很久了,但是它也有自己的局限性,如卷积算子较小的感受野限制了长距离建模能力,而 transformer 中的自我注意机制拓宽了感受野,可以提高视频识别的性能。而...
find the Ground Truth sequence with the lowest Lmatch as its supervision. According to the corresponding supervision information, the loss function of the entire network can be calculated. Since our method is to implement classification, detection, segmentation and tracking into an end-to-end network...
//arxiv.org/abs/2004.04730Audiovisual SlowFast networks for video recognitionhttps://arxiv.org/abs/2001.08740Non-local neural networkshttps://arxiv.org/abs/1711.07971A closer look at spatiotemporal convolutions for action recognitionhttps://arxiv.org/abs/1711.11248Video classification with ...
Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. 展开 关键词:Current transformers Visualization Data models Training Tokenization Market research ...
Considering recent progress in classifying gastrointestinal anomalies and landmarks in endoscopic and video capsule endoscopy images, this study proposes a hybrid model incorporating the advantages of Transformers and Convolutional Neural Networks (CNNs) for enhanced classification performance. Our model ...
3. Action Classification on Kinetics-400 https://paperswithcode.com/sota/action-classification-on-kinetics-400?tag_filter=163 4. Self-Supervised Action Recognition on UCF101 https://paperswithcode.com/sota/self-supervised-action-recognition-on-ucf101?tag_filter=163 ...
* [推荐]题目: Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers* PDF: arxiv.org/abs/2303.0916* 作者: Jia Li,Yin Chen,Xuesong Zhang,Jiantao Nie,Yangchen Yu,Ziqiang Li,Meng Wang,Richang Hong* 其他: ...
* Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis* 链接: arxiv.org/abs/2207.1285* 作者: Tomoki Uchiyama,Naoya Sogi,Koichiro Niinuma,Kazuhiro Fukui* 其他: 10 pages* 摘要: 本文提出了一种通过视觉解释3D卷积神经网络(CNN)的决策过程的方法,并...