Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

TL;DR

This survey reviews recent advances in making deep neural networks more efficient for visual recognition tasks, emphasizing both model and data perspectives across images, videos, and point data.

Contribution

It provides a systematic summary of recent efficiency improvements in DNN-based visual recognition, including new directions and a focus on data-centric approaches.

Findings

Comprehensive overview of recent efficiency techniques for DNNs in visual recognition.

Analysis of data types: images, videos, and points, for efficiency improvements.

Suggestions for future research directions in DNN efficiency.

Abstract

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems.