Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning

With the rapid development of wearable cameras, a massive collection of egocentric video for first-person visual perception becomes available. Using egocentric videos to predict first-person activity faces many challenges, including limited field of view, occlusions, and unstable motions. Observing that sensor data from wearable devices facilitates human activity recognition, multi-modal activity recognition is attracting increasing attention. However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition. Nowadays, deep learning in real world has led to a focus on continual learning that often suffers from catastrophic forgetting. But the catastrophic forgetting problem for egocentric activity recognition, especially in the context of multiple modalities, remains unexplored due to unavailability of dataset. In order to assist this research, we present a multi-modal egocentric activity dataset for continual learning named UESTC-MMEA-CL, which is collected by self-developed glasses integrating a first-person camera and wearable sensors. It contains synchronized data of videos, accelerometers, and gyroscopes, for 32 types of daily activities, performed by 10 participants. Its class types and scale are compared with other publicly available datasets. The statistical analysis of the sensor data is given to show the auxiliary effects for different behaviors. And results of egocentric activity recognition are reported when using separately, and jointly, three modalities: RGB, acceleration, and gyroscope, on a base network architecture. To explore the catastrophic forgetting in continual learning tasks, four baseline methods are extensively evaluated with different multi-modal combinations. We hope the UESTC-MMEA-CL can promote future studies on continual learning for first-person activity recognition in wearable applications.

A Review of 3D Face Reconstruction From a Single Image

3D face reconstruction is a challenging problem but also an important task in the field of computer vision and graphics. Recently, many researchers put attention to the problem and a large number of articles have been published. Single image reconstruction is one of the branches of 3D face reconstruction, which has a lot of applications in our life. This paper is a review of the recent literature on 3D face reconstruction from a single image.

A Literature Review of 3D Face Reconstruction From a Single Image

This paper is a brief survey of the recent literature on 3D face reconstruction from a single image. Most articles have been choosen among 2016 and 2020, in order to provide the most up-to-date view of the single image 3D face reconstruction.