A defining characteristic of natural vision is its ability to withstand a
variety of input alterations, resulting in the creation of an invariant
representation of the surroundings. While convolutional neural networks exhibit
resilience to certain forms of spatial input variation, modifications in the
spatial and temporal aspects can significantly affect the representations of
video content in deep neural networks. Inspired by the resilience of natural
vision to input variations, we employ a simple multi-stream model to explore
its potential to address spatiotemporal changes by including temporal features.
Our primary goal is to introduce a video-trained model and evaluate its
robustness to diverse image and video inputs, with a particular focus on
exploring the role of temporal features in invariant recognition. Results show
that including videos and the temporal stream during training mitigates the
decline in accuracy and mAP in image and video understanding tasks by 1.36% and
3.14%, respectively.

通过多流模型，我们引入一个经过视频训练的模型，并评估其在各种图像和视频输入下的鲁棒性，特别关注时间特征在不变识别中的作用。结果表明，在训练中包含视频和时间流能够抑制图像和视频理解任务中准确性和平均准确率（mAP）下降分别约 1.36％和 3.14％。