Efficient Methods for the Design and Training of Neural Networks

The field of artificial intelligence has seen significant advancements with the development of neural networks, which have numerous applications in computer vision, natural language processing, and speech processing. Despite these advancements, designing and training these networks still pose numerous challenges. This thesis aims to address two critical aspects of neural network development, design and training, within the context of computer vision tasks. The thesis focuses on three main challenges in the development of neural networks. The first challenge is finding an efficient way to perform architecture search in an extremely large or even unlimited search space. To address this challenge, the thesis proposes a Neural Search-space Evolution (NSE) scheme that enables efficient and effective architecture search in large-scale search spaces. The second challenge is to improve the efficiency of self-supervised learning for model pretraining. To address this challenge, the thesis proposes a combinatorial patches approach that significantly improves the efficiency of self-supervised learning. The third challenge is to develop an efficient and versatile multitask model that can leverage the benefits of large-scale multitask training. To address this challenge, the thesis proposes a Unified model for Human-Centric Perceptions (UniHCP) as a simple and scalable solution for a human-centric perception system that unifies multiple human-centric tasks into a neat, efficient, and scalable model. The results of this thesis demonstrate the effectiveness of the proposed methods in improving the practicality and performance of neural network design and training. The NSE scheme, combinatorial patches approach, and UniHCP have been tested on a broad range of datasets, tasks, and settings, yielding impressive results. These findings affirm the efficacy of the proposed methods in enhancing the efficiency of the design and training process of neural networks

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio