In this study, we propose a method for jointly learning of images and videos
using a single model. In general, images and videos are often trained by
separate models. We propose in this paper a method that takes a batch of images
as input to Vision Transformer IV-ViT, and also a set of video frames with
temporal aggregation by late fusion. Experimental results on two image datasets
and two action recognition datasets are presented.

我们提出了一种使用单一模型联合学习图像和视频的方法，该方法采用了批量图像输入和通过深度融合来进行时间聚合的视频帧集合。通过实验结果展示了在两个图像数据集和两个动作识别数据集上的效果。

单一视觉变换器的图像和视频联合学习

Joint learning of images and videos with a single Vision Transformer

Purpose: The aim of this work is to introduce a single model-based deep
network that can provide high-quality reconstructions from undersampled
parallel MRI data acquired with multiple sequences, acquisition settings and
field strengths.
Methods: A single unrolled architecture, which offers good reconstructions
for multiple acquisition settings, is introduced. The proposed scheme adapts
the model to each setting by scaling the CNN features and the regularization
parameter with appropriate weights. The scaling weights and regularization
parameter are derived using a multi-layer perceptron model from conditional
vectors, which represents the specific acquisition setting. The perceptron
parameters and the CNN weights are jointly trained using data from multiple
acquisition settings, including differences in field strengths, acceleration,
and contrasts. The conditional network is validated using datasets acquired
with different acquisition settings.
Results: The comparison of the adaptive framework, which trains a single
model using the data from all the settings, shows that it can offer
consistently improved performance for each acquisition condition. The
comparison of the proposed scheme with networks that are trained independently
for each acquisition setting shows that it requires less training data per
acquisition setting to offer good performance.
Conclusion: The Ada-MoDL framework enables the use of a single model-based
unrolled network for multiple acquisition settings. In addition to eliminating
the need to train and store multiple networks for different acquisition
settings, this approach reduces the training data needed for each acquisition
setting.

此研究介绍了一种针对不同磁场强度、加速和对比度等多种采集条件的 MRI 数据的单一基于模型的深度神经网络，可提供高质量的重建。通过使用一个可滚动的体系结构，基于每个捕获条件调整 CNN 特征和正则化参数的权重，利用来自多个采集条件的数据共同训练模型，证明该模型可以始终提供更好的性能，同时减少了每个采集设置所需的训练数据。

Ada-MoDL：将基于模型的深度学习适应于多种获取条件

Adapting model-based deep learning to multiple acquisition conditions:  Ada-MoDL

Model ensemble techniques often increase task performance in neural networks;
however, they require increased time, memory, and management effort. In this
study, we propose a novel method that replicates the effects of a model
ensemble with a single model. Our approach creates K-virtual models within a
single parameter space using K-distinct pseudo-tags and K-distinct vectors.
Experiments on text classification and sequence labeling tasks on several
datasets demonstrate that our method emulates or outperforms a traditional
model ensemble with 1/K-times fewer parameters.

本文提出了一种使用单一模型来复制模型集成效果的方法，通过使用虚拟模型和不同向量的伪标记来创造 K 个虚拟模型，实验证明该方法可以在文本分类等任务中与传统模型集成相当或更好的性能，同时参数量减少了 1/K。