配置管理详解:基线定义与作用

需积分: 2 5 下载量 38 浏览量 更新于2024-08-23 1 收藏 1.56MB PPT 举报
"本文主要介绍了基线(Baseline)在配置管理过程中的概念和作用,以及配置管理工具的应用。基线是经过正式评审的配置项(CI)的状态,用于控制和跟踪项目的开发进程。配置管理是管理和指导软件开发,减少混乱,确保版本控制、协同工作和变更管理的有效性。配置管理涉及配置项的标识、状态报告、变更控制、完整性验证等职责,对于防止版本混乱、协同问题和缺陷重复出现具有重要意义。" 在配置管理过程中,基线是一个关键概念。它代表了一个或多个配置项(CI)在特定时间点上的稳定状态,经过正式审查后被固定下来,作为后续开发的基础。基线通常在项目的关键里程碑处创建,与项目进度保持一致。每个基线都确定了一个特定的配置项版本,确保了版本的唯一性。配置管理的目标是通过标识、组织和控制修改来提高生产效率,确保配置项的功能特性和物理特性在整个生命周期内的可控性。 配置管理工具的使用至关重要,它们帮助实现以下功能: 1. 配置项标识:明确项目中的工作产品,定义命名和标识规则,确保在组织内部的一致性,便于检索和管理。 2. 配置状态报告:提供基线内容和状态的信息,让团队了解变更情况,促进资源共享。 3. 变更管理:定义变更流程,设立变更控制委员会(CCB),对不同级别的变更进行分类和分层处理,平衡项目自主权与决策效率。 4. 配置审计:验证配置项的完整性和正确性,确保所有变更都符合规定。 配置管理的实践还包括对配置库的目录结构设计,以逻辑清晰的方式组织配置项,便于管理和查找。此外,变更控制是配置管理的核心任务之一,有效的变更管理需要明确的流程和自动化工具的支持,以确保变更的及时审批和实施。 配置管理的重要性在于,它能预防因忽视而导致的问题,如标识混乱、版本错乱、协作障碍、已解决缺陷的重现等。通过实施配置管理,项目可以更有效地管理变更,保持工作产品的完整性和一致性,提高团队的生产力和项目成功率。
2020-05-27 上传
Human parsing has been extensively studied recently (Yamaguchi et al. 2012; Xia et al. 2017) due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient crossdomain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem. Abstract—This paper presents a robust Joint Discriminative appearance model based Tracking method using online random forests and mid-level feature (superpixels). To achieve superpixel- wise discriminative ability, we propose a joint appearance model that consists of two random forest based models, i.e., the Background-Target discriminative Model (BTM) and Distractor- Target discriminative Model (DTM). More specifically, the BTM effectively learns discriminative information between the target object and background. In contrast, the DTM is used to suppress distracting superpixels which significantly improves the tracker’s robustness and alleviates the drifting problem. A novel online random forest regression algorithm is proposed to build the two models. The BTM and DTM are linearly combined into a joint model to compute a confidence map. Tracking results are estimated using the confidence map, where the position and scale of the target are estimated orderly. Furthermore, we design a model updating strategy to adapt the appearance changes over time by discarding degraded trees of the BTM and DTM and initializing new trees as replacements. We test the proposed tracking method on two large tracking benchmarks, the CVPR2013 tracking benchmark and VOT2014 tracking challenge. Experimental results show that the tracker runs at real-time speed and achieves favorable tracking performance compared with the state-of-the-art methods. The results also sug- gest that the DTM improves tracking performance significantly and plays an important role in robust tracking.