Various research studies indicate that action recognition performance highly
depends on the types of motions being extracted and how accurate the human
actions are represented. In this paper, we investigate different optical flow,
and features extracted from these optical flow that capturing both short-term
and long-term motion dynamics. We perform power normalization on the magnitude
component of optical flow for flow dynamics correction to boost subtle or
dampen sudden motions. We show that existing action recognition models which
rely on optical flow are able to get performance boosted with our corrected
optical flow. To further improve performance, we integrate our corrected flow
dynamics into popular models through a simple hallucination step by selecting
only the best performing optical flow features, and we show that by
'translating' the CNN feature maps into these optical flow features with
different scales of motions leads to the new state-of-the-art performance on
several benchmarks including HMDB-51, YUP++, fine-grained action recognition on
MPII Cooking Activities, and large-scale Charades.

基于光流和动作识别，本文研究不同类型的光流及从光流中提取的特征，通过对光流动态进行功率归一化以纠正流动态并提高性能，进一步将修正后的流动态集成到流行模型中，并通过简单的幻觉步骤翻译 CNN 特征图到不同尺度的光流特征，取得多个基准测试中的最佳性能。

动作识别的流动力学校正

Flow Dynamics Correction for Action Recognition

Self-supervised learning (SSL) methods such as masked language modeling have
shown massive performance gains by pretraining transformer models for a variety
of natural language processing tasks. The follow-up research adapted similar
methods like masked image modeling in vision transformer and demonstrated
improvements in the image classification task. Such simple self-supervised
methods are not exhaustively studied for object detection transformers (DETR,
Deformable DETR) as their transformer encoder modules take input in the
convolutional neural network (CNN) extracted feature space rather than the
image space as in general vision transformers. However, the CNN feature maps
still maintain the spatial relationship and we utilize this property to design
self-supervised learning approaches to train the encoder of object detection
transformers in pretraining and multi-task learning settings. We explore common
self-supervised methods based on image reconstruction, masked image modeling
and jigsaw. Preliminary experiments in the iSAID dataset demonstrate faster
convergence of DETR in the initial epochs in both pretraining and multi-task
learning settings; nonetheless, similar improvement is not observed in the case
of multi-task learning with Deformable DETR. The code for our experiments with
DETR and Deformable DETR are available at this https URL
and this https URL respectively.

研究使用自监督学习中的图像重构、遮蔽图像建模和拼图等方法来训练物体检测转换器的编码器，以实现预训练和多任务学习，并在 iSAID 数据集上进行了初步实验。

自监督学习在利用 Transformer 进行物体检测方面的实证研究

An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers

Convolutional Neural Networks (CNNs) are powerful models that achieve
impressive results for image classification. In addition, pre-trained CNNs are
also useful for other computer vision tasks as generic feature extractors. This
paper aims to gain insight into the feature aspect of CNN and demonstrate other
uses of CNN features. Our results show that CNN feature maps can be used with
Random Forests and SVM to yield classification results that outperforms the
original CNN. A CNN that is less than optimal (e.g. not fully trained or
overfitting) can also extract features for Random Forest/SVM that yield
competitive classification accuracy. In contrast to the literature which uses
the top-layer activations as feature representation of images for other tasks,
using lower-layer features can yield better results for classification.

本文旨在深入探究 CNN 中的特征方面，发现 CNN 特征映射可用于随机森林和 SVM，以产生超越原始 CNN 的分类结果；使用较低层次的特征可以获得更好的分类结果。