In Recent years, with the vast development of deep learning techniques, a great deal of effort has been devoted in the computer vision and multimedia community toward the problems of visual object analysis, such as object representation, recognition, detection, identification, etc. Especially at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, the computers have successfully outperformed humans with a lower error rate at image recognition for the first time. However, most of existing algorithms focus more on analyzing objects in a relatively simple and restricted situation, which may perform poorly in natural environments.

中文翻译：

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复杂场景中的上下文对象分析专题介绍

近年来，随着深度学习技术的飞速发展，计算机视觉和多媒体界在视觉对象分析问题上投入了大量精力，如对象表示、识别、检测、识别等。在 2014 年的 ImageNet 大规模视觉识别挑战赛 (ILSVRC) 中，计算机首次成功地以较低的图像识别错误率超越了人类。然而，大多数现有算法更侧重于在相对简单和受限的情况下分析对象，这在自然环境中可能表现不佳。