Visual content analysis and understanding attract tremendous attention because of its potentially wide range of applications including human activity analysis, automated photo face tagging, multicamera tracking, crowded counting, and biometric security. With recent progress in end-to-end differentiable learning, the accuracy of algorithms has been significantly improved and even outperforms humans in some tasks. In addition, multimodality methods, targeting on making full use of various visual data sources, are further investigated. These developments contribute to the innovations of two core modules for a typical intelligent vision system, i.e., image and video description and recognition, which are critical for the success of the visual content analysis and understanding in more complex and challenging open world.

中文翻译：

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智能视觉内容分析与理解专区特邀编辑介绍

视觉内容分析和理解因其潜在的广泛应用而引起了极大的关注，包括人类活动分析、自动照片面部标记、多相机跟踪、拥挤计数和生物识别安全。随着端到端可微学习的最新进展，算法的准确性得到了显着提高，甚至在某些任务中超过了人类。此外，进一步研究了以充分利用各种视觉数据源为目标的多模态方法。这些发展有助于典型智能视觉系统的两个核心模块的创新，即图像和视频描述和识别，这对于在更复杂和更具挑战性的开放世界中视觉内容分析和理解的成功至关重要。