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Research Problem: Identification and Formulation

Research is an investigation or experimentation that is aimed at a discovery and interpretation of facts, revision of theories or laws or practical application of the new or revised theories or laws. Identification of research problem leads in conducting a research. To initiate a research, the necessity for the research, to be carried out should be generated.The ideas and topics are developed while consulting literatures, discussions with experts and continuation of activities related to the subject matter. These ideas/topics generally called research problems and are statements about areas of concern, a condition to be improved, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or in practice that points to the need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. The problem must be significant researchable lead to further research and suitable for the researcher. Formulation of the problem should lead to empirical investigation. Formulation of research problem should depict what is to be determined and scope of the study.It also involves key concept definitions questions to be asked. The objective of the present paper highlights the above stated issues.

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Lightweight Human Pose Estimation Based on Multi-Attention Mechanism

基于多注意力机制的轻量化人体姿态估计

• Published: 02 January 2024

Cite this article

• Xiao Lin  ( 林 晓 ) 1 , 2 , 3 ,
• Meichen Lu  ( 陆美晨 ) 1 , 3 ,
• Mufeng Gao  ( 高幕峰 ) 4 &
• Yan Li  ( 李 岩 ) 1 , 2  

148 Accesses

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Human pose estimation has received much attention from the research community because of its wide range of applications. However, current research for pose estimation is usually complex and computationally intensive, especially the feature loss problems in the feature fusion process. To address the above problems, we propose a lightweight human pose estimation network based on multi-attention mechanism (LMANet). In our method, network parameters can be significantly reduced by lightweighting the bottleneck blocks with depth-wise separable convolution on the high-resolution networks. After that, we also introduce a multi-attention mechanism to improve the model prediction accuracy, and the channel attention module is added in the initial stage of the network to enhance the local cross-channel information interaction. More importantly, we inject spatial cross-awareness module in the multi-scale feature fusion stage to reduce the spatial information loss during feature extraction. Extensive experiments on COCO2017 dataset and MPII dataset show that LMANet can guarantee a higher prediction accuracy with fewer network parameters and computational effort. Compared with the high-resolution network HRNet, the number of parameters and the computational complexity of the network are reduced by 67% and 73%, respectively.

人体姿态估计因其广泛的应用场景而受到研究界的关注, 然而现有的网络结构通常结构复杂计算量大, 并且在特征融合过程中存在特征丢失问题. 针对上述问题, 提出一个轻量化的基于多重注意力机制的人体姿态估计网络LMANet. 在高分辨率网络的基础上利用深度可分离卷积对瓶颈块进行轻量化处理, 能够大幅度减少网络参数量; 之后引入多重注意力机制提高模型预测精度, 在网络初始阶段加入通道注意力模块增强局部跨通道的信息交互; 在多尺度特征融合阶段引入空间注意力机制, 通过空间交叉感知模块减少特征提取过程中空间信息损失. 在COCO2017数据集和MPII数据集上的实验结果表明, LMANet能够在较少的参数量和计算量的情况下保证较高的预测精确度; 相较于高分辨率网络HRNet, 网络的参数量和计算复杂度分别减少67%和73%.

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Attention Refined Network for Human Pose Estimation

Enhancing multi-scale information exchange and feature fusion for human pose estimation

An improved lightweight high-resolution network based on multi-dimensional weighting for human pose estimation

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Institute of Artificial Intelligence on Education Research, College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China

Xiao Lin  ( 林 晓 ), Meichen Lu  ( 陆美晨 ) & Yan Li  ( 李 岩 )

Shanghai Intelligent Education Big Data Engineering Technology Research Center, Shanghai Normal University, Shanghai, 200234, China

Xiao Lin  ( 林 晓 ) & Yan Li  ( 李 岩 )

Shanghai Online Education Research Base for Primary and Secondary Schools, Shanghai, 200234, China

Xiao Lin  ( 林 晓 ) & Meichen Lu  ( 陆美晨 )

School of Physical Education, Shanghai Normal University, Shanghai, 200234, China

Mufeng Gao  ( 高幕峰 )

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Correspondence to Yan Li  ( 李 岩 ) .

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Foundation item: the National Natural Science Foundation of China (Nos. 61775139, 62072126, 61772164, and 61872242)

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Lin, X., Lu, M., Gao, M. et al. Lightweight Human Pose Estimation Based on Multi-Attention Mechanism. J. Shanghai Jiaotong Univ. (Sci.) (2024). https://doi.org/10.1007/s12204-023-2691-y

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Received : 03 August 2023

Accepted : 24 August 2023

Published : 02 January 2024

DOI : https://doi.org/10.1007/s12204-023-2691-y

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Transformations That Work

• Michael Mankins
• Patrick Litre

More than a third of large organizations have some type of transformation program underway at any given time, and many launch one major change initiative after another. Though they kick off with a lot of fanfare, most of these efforts fail to deliver. Only 12% produce lasting results, and that figure hasn’t budged in the past two decades, despite everything we’ve learned over the years about how to lead change.

Clearly, businesses need a new model for transformation. In this article the authors present one based on research with dozens of leading companies that have defied the odds, such as Ford, Dell, Amgen, T-Mobile, Adobe, and Virgin Australia. The successful programs, the authors found, employed six critical practices: treating transformation as a continuous process; building it into the company’s operating rhythm; explicitly managing organizational energy; using aspirations, not benchmarks, to set goals; driving change from the middle of the organization out; and tapping significant external capital to fund the effort from the start.

Lessons from companies that are defying the odds

Idea in Brief

The problem.

Although companies frequently engage in transformation initiatives, few are actually transformative. Research indicates that only 12% of major change programs produce lasting results.

Why It Happens

Leaders are increasingly content with incremental improvements. As a result, they experience fewer outright failures but equally fewer real transformations.

The Solution

To deliver, change programs must treat transformation as a continuous process, build it into the company’s operating rhythm, explicitly manage organizational energy, state aspirations rather than set targets, drive change from the middle out, and be funded by serious capital investments.

Nearly every major corporation has embarked on some sort of transformation in recent years. By our estimates, at any given time more than a third of large organizations have a transformation program underway. When asked, roughly 50% of CEOs we’ve interviewed report that their company has undertaken two or more major change efforts within the past five years, with nearly 20% reporting three or more.

• Michael Mankins is a leader in Bain’s Organization and Strategy practices and is a partner based in Austin, Texas. He is a coauthor of Time, Talent, Energy: Overcome Organizational Drag and Unleash Your Team’s Productive Power (Harvard Business Review Press, 2017).
• PL Patrick Litre leads Bain’s Global Transformation and Change practice and is a partner based in Atlanta.

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