不是每一个补丁都需要:为基于视频的个人重新识别提供更有效和更有效的骨干
概括
这项研究引入了基于视频的个人重新识别 (ReID) 的高效骨干. 它通过选择性地提取特征和使用伪全球上下文来减少计算,以更低的成本实现高精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于视频的个人重新识别 (ReID) 通常依赖于从所有视频中详尽的特征提取.
- 使用卷积神经网络 (CNN) 或视觉转换器 (ViT) 的传统方法可能是计算密集的.
- 由于微小的人类运动,ReID视频中的相似性表明了计算优化的潜力.
研究的目的:
- 为基于视频的个人ReID提出一种新,高效和有效的插件和播放骨干.
- 为了降低与传统的ReID特征提取方法相关的计算成本.
- 为了保持或提高准确性,同时显著降低计算需求.
主要方法:
- 引入了一个补丁选择机制,只从关键的,不重复的图像补丁中提取特征.
- 开发了一个新的网络结构来生成和利用伪框架全球上下文.
- 纠正来自稀疏输入数据的不完整视图.
主要成果:
- 实现了显著的计算成本降低:与ViT-B相比降低了74%,与ResNet50相比降低了28%.
- 保持了与ViT-B.可比的准确性.
- 与ResNet50.50.相比,证明了更高的准确性
- 拟议的骨干提供了高性能和低计算成本.
结论:
- 拟议的选择性特征提取和伪全球上下文方法对基于视频的人ReID有效.
- 这种方法为传统的详尽特征提取技术提供了计算效率高的替代方案.
- 脊柱为ReID任务提供了准确性和计算成本之间的强大的平衡.
相关概念视频
01:22Force Classification
1.1K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.1K
00:56Extraction: Advanced Methods
398
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
398
01:20Deconvolution
127
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
127