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学者姓名：王立春
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Abstract ：
Scene graph generation (SGG) aims to perceive objects and their relations in images, which can bridge the gap between upstream detection tasks and downstream high-level visual understanding tasks. For SGG models, over-fitting head predicates can lead to bias in the generated scene graph, which has become a consensus. A series of debiasing methods have been proposed to solve the problem. However, some existing debiasing SGG methods have a tendency to over-fit tail predicates, which is another type of bias. In order to eliminate the one-way over-fitting of head or tail predicates, this article proposes a balanced relation prediction (BRP) module which is model-agnostic and compatible with existing re-balancing methods. Moreover, because the relation prediction is based on object feature representation, this article proposes a scene adaptive context fusion (SACF) module to refine the object feature representation. Specifically, SACF models the context based on a chain structure, where the order of objects in the chain structure is adaptively arranged according to the scene content, achieving visual information fusion that adapts to the scene where the objects are located. Experiments on VG and GQA datasets show that the proposed method achieves competitive results on the comprehensive metric of R@K and mR@K.
Keyword ：
Deep Network Deep Network Scene Graph Generation Scene Graph Generation Balanced Relation Prediction Balanced Relation Prediction Scene Adaptive Context Modeling Scene Adaptive Context Modeling
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| GB/T 7714 | Xu, Kai , Wang, Lichun , Li, Shuang et al. Scene Adaptive Context Modeling and Balanced Relation Prediction for Scene Graph Generation [J]. | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (3) . |
| MLA | Xu, Kai et al. "Scene Adaptive Context Modeling and Balanced Relation Prediction for Scene Graph Generation" . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 21 . 3 (2025) . |
| APA | Xu, Kai , Wang, Lichun , Li, Shuang , Gao, Tong , Yin, Baocai . Scene Adaptive Context Modeling and Balanced Relation Prediction for Scene Graph Generation . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (3) . |
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Abstract ：
When robots carry out task, selecting an appropriate tool is necessary. The current research ignores the fine-grained characteristic of tasks, and mainly focuses on whether the task can be completed. Little consideration is paid for the object being manipulated, which affects the task completion quality. In order to support task oriented fine-grained tool recommendation, based on common sense knowledge, this paper proposes Fine-grained Tool-Task Graph (FTTG) to describe multi-granularity semantics of tasks, tools, objects being manipulated and relationships among them. According to FTTG, a Fine-grained Tool-Task (FTT) dataset is constructed by labeling images of tools and objects being manipulated with the defined semantics. A baseline method named Fine-grained Tool Recommendation Network (FTR-Net) is also proposed in this paper. FTR-Net gives coarse-grained and fine-grained semantic predictions by simultaneously learning the common and special features of the tools and objects being manipulated. At the same time, FTR-Net constrains the distance between features of the well matched tool and object more smaller than that of those unmatched. The constraint and the special feature ensure FTR-Net provide fine-grained tool recommendation. The constraint and the common feature ensure FTR-Net provide coarse-grained tool recommendation when the fine-grained tools are not available. Experiments show that FTR-Net can recommend tools consistent with common sense whether on test data sets or in real situations.
Keyword ：
data sets for robotic vision data sets for robotic vision deep learning for visual perception deep learning for visual perception Computer vision for automation Computer vision for automation
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| GB/T 7714 | Xin, Jianjia , Wang, Lichun , Wang, Shaofan et al. Recommending Fine-Grained Tool Consistent With Common Sense Knowledge for Robot [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2022 , 7 (4) : 8574-8581 . |
| MLA | Xin, Jianjia et al. "Recommending Fine-Grained Tool Consistent With Common Sense Knowledge for Robot" . | IEEE ROBOTICS AND AUTOMATION LETTERS 7 . 4 (2022) : 8574-8581 . |
| APA | Xin, Jianjia , Wang, Lichun , Wang, Shaofan , Liu, Yukun , Yang, Chao , Yin, Baocai . Recommending Fine-Grained Tool Consistent With Common Sense Knowledge for Robot . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2022 , 7 (4) , 8574-8581 . |
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Abstract ：
一种基于点空洞方向卷积的点云语义分割方法适用于计算机视觉领域。它是一种交替使用点空洞方向卷积模块，边缘保持池化模块和边缘保持非池化模块的分层编解码网络。点空洞方向编码单元能通过改变空洞率来对邻域点进行等效稀疏采样，同时考虑了局部邻域点的方向信息和距离信息，可以在编码八个方向特征信息的同时任意的扩大其感受野，从而更全面地捕捉局部邻域信息。然后，将多个点空洞方向编码单元堆叠在一起组成点空洞方向卷积模块，该模块具有尺度感知能力和可移植性。边缘保持池化模块和边缘保持非池化模块用来保留边缘特征，恢复点云的高维特征，提高点云语义分割精度。该方法包括点云的局部邻域选择与特征提取，以获得更好的语义分割性能。
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| GB/T 7714 | 王少帆 , 刘蓥 , 王立春 et al. 一种基于点空洞方向卷积的点云语义分割方法 : CN202210400811.5[P]. | 2022-04-17 . |
| MLA | 王少帆 et al. "一种基于点空洞方向卷积的点云语义分割方法" : CN202210400811.5. | 2022-04-17 . |
| APA | 王少帆 , 刘蓥 , 王立春 , 孙艳丰 , 尹宝才 . 一种基于点空洞方向卷积的点云语义分割方法 : CN202210400811.5. | 2022-04-17 . |
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Abstract ：
一种用于场景图生成的自适应上下文建模方法及装置，可以根据场景内容自适应地序列化其中的物体，从而改善生成的场景图的效果。方法包括：(1)使用经过预训练的目标检测器对输入图像进行目标检测，输出一系列物体提议，选择置信度较高的前80个，将其视为该场景中存在的物体；(2)将细化后的语义标签映射为200维的向量表示，然后将其与物体的视觉特征以及上下文特征拼接起来作为物体的完整特征表示，将图像中n个物体的特征O分别输入物体选择位置分支和位置选择物体分支，衡量物体与其在链式结构中的位置的匹配程度，计算得到物体与位置的匹配分数矩阵，对物体的序列化问题看作指派问题来求解；(3)上下文信息融合以及关系预测。
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| GB/T 7714 | 王立春 , 徐凯 , 尹宝才 . 一种用于场景图生成的自适应上下文建模方法及装置 : CN202211008807.0[P]. | 2022-08-22 . |
| MLA | 王立春 et al. "一种用于场景图生成的自适应上下文建模方法及装置" : CN202211008807.0. | 2022-08-22 . |
| APA | 王立春 , 徐凯 , 尹宝才 . 一种用于场景图生成的自适应上下文建模方法及装置 : CN202211008807.0. | 2022-08-22 . |
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Abstract ：
Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4(2), and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.
Keyword ：
sparse representation sparse representation Action recognition Action recognition transfer learning transfer learning multi-view multi-view
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| GB/T 7714 | Sun, Bin , Kong, Dehui , Wang, Shaofan et al. Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
| MLA | Sun, Bin et al. "Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 2 (2021) . |
| APA | Sun, Bin , Kong, Dehui , Wang, Shaofan , Wang, Lichun , Yin, Baocai . Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (2) . |
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Abstract ：
针对理工科课程在教学内容、方式上实施"润物细无声"式课程思政的难度,以计算机图形学为例,提出课程思政建设总体思路,在阐述具体课程思政建设过程及结果的基础上,凝练总体建设原则,给出一般化的理工科课程思政建设策略,为在理工科院校更广泛、更有效地开展课程思政建设提供思路。
Keyword ：
计算机图形学 计算机图形学 课程思政 课程思政 科学方法论 科学方法论 理工科课程思政建设 理工科课程思政建设 内涵与外延 内涵与外延
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| GB/T 7714 | 孔德慧 , 李敬华 , 王立春 et al. 基于计算机图形学教学实践的理工科课程思政建设研究 [J]. | 计算机教育 , 2021 , PageCount-页数: 4 (09) : 15-18 . |
| MLA | 孔德慧 et al. "基于计算机图形学教学实践的理工科课程思政建设研究" . | 计算机教育 PageCount-页数: 4 . 09 (2021) : 15-18 . |
| APA | 孔德慧 , 李敬华 , 王立春 , 张勇 , 孙艳丰 . 基于计算机图形学教学实践的理工科课程思政建设研究 . | 计算机教育 , 2021 , PageCount-页数: 4 (09) , 15-18 . |
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Abstract ：
基于交互任务知识图谱的细粒度工具推荐方法及装置，能够很好地针对细粒度任务进行工具推荐，并且在最优工具不存在时，可以有效地检索到替代工具。方法包括：(1)建立交互任务知识图谱ITKG来定义交互任务、工具及被操作物体的多粒度语义；(2)通过交互工具推荐网络IT‑Net推荐细粒度任务适配的工具；(3)通过约束工具和被操作物体的粗粒度语义预测损失loss，通过细粒度语义预测loss，使IT‑Net学习到工具和被操作物体的共同特征和专有特征；(4)通过约束适配细粒度任务的工具和被操作物体的嵌入特征距离小于不适配细粒度任务的工具和被操作物体的嵌入特征距离，使IT‑Net学习适配细粒度任务的工具和被操作物体的特征关系。
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| GB/T 7714 | 王立春 , 信建佳 , 王少帆 et al. 基于交互任务知识图谱的细粒度工具推荐方法及装置 : CN202111310620.1[P]. | 2021-11-04 . |
| MLA | 王立春 et al. "基于交互任务知识图谱的细粒度工具推荐方法及装置" : CN202111310620.1. | 2021-11-04 . |
| APA | 王立春 , 信建佳 , 王少帆 , 李敬华 , 孔德慧 , 尹宝才 . 基于交互任务知识图谱的细粒度工具推荐方法及装置 : CN202111310620.1. | 2021-11-04 . |
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Abstract ：
公开一种面向零样本识别的字典学习方法及装置，可以从类别层面和图像层面分别建立视觉空间和语义空间之间的对齐，从而实现高精度的零样本图像识别。方法包括：(1)基于跨域字典学习方法训练类别层的跨域字典；(2)基于步骤(1)学习的类别层跨域字典生成图像的语义属性；(3)基于步骤(2)生成的图像语义属性训练图像层的跨域字典；(4)基于步骤(3)学习的图像层跨域字典完成对不可见类别图像的识别任务。
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| GB/T 7714 | 王立春 , 李爽 , 王少帆 et al. 一种面向零样本识别的字典学习方法及装置 : CN202111237748.X[P]. | 2021-10-22 . |
| MLA | 王立春 et al. "一种面向零样本识别的字典学习方法及装置" : CN202111237748.X. | 2021-10-22 . |
| APA | 王立春 , 李爽 , 王少帆 , 孔德慧 , 尹宝才 . 一种面向零样本识别的字典学习方法及装置 : CN202111237748.X. | 2021-10-22 . |
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Abstract ：
一种RGB‑D信息互补的语义分割方法属于图像分割技术领域。本发明针对已有利用RGB和深度信息的方法只考虑单向补充的问题，提出一种RGB和深度信息交叉互补的RGB‑D语义分割网络结构，旨在对RGB和深度信息进行双向的逐层信息补充，达到提高语义分割效果的目的。
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| GB/T 7714 | 王立春 , 顾娜娜 , 王少帆 et al. 一种RGB-D信息互补的语义分割方法 : CN202111009283.2[P]. | 2021-08-31 . |
| MLA | 王立春 et al. "一种RGB-D信息互补的语义分割方法" : CN202111009283.2. | 2021-08-31 . |
| APA | 王立春 , 顾娜娜 , 王少帆 , 杨臣 , 信建佳 , 尹宝才 . 一种RGB-D信息互补的语义分割方法 : CN202111009283.2. | 2021-08-31 . |
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Abstract ：
本发明提出一种基于排序与语义一致性约束的实例分割改进方法，针对如何提高分割实例的掩膜质量问题，主要是提出面向实例分割网络的排序损失与语义一致性损失，前者优化子区域的选择结果，后者优化子区域的语义分割结果。实例分割属于计算机视觉领域的重要任务，既要求区分具体实例，又要求完成分类与定位任务。当前的实例分割方法，存在分割实例的掩膜质量不高的问题，这对很多实际任务有不可忽略的负面影响。提出的排序损失与语义一致性损失，可以应用于目前已有的任意两阶段与单阶段实例分割框架中。在公开数据集上进行的实验表明，增加排序损失与语义一致性损失后，深度网络的实例分割效果均取得一定程度的提升，分割实例的掩膜质量有所改善。
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| GB/T 7714 | 王立春 , 杨臣 , 王少帆 et al. 一种基于排序与语义一致性约束的实例分割改进方法 : CN202110608265.X[P]. | 2021-06-01 . |
| MLA | 王立春 et al. "一种基于排序与语义一致性约束的实例分割改进方法" : CN202110608265.X. | 2021-06-01 . |
| APA | 王立春 , 杨臣 , 王少帆 , 孔德慧 , 李敬华 , 尹宝才 . 一种基于排序与语义一致性约束的实例分割改进方法 : CN202110608265.X. | 2021-06-01 . |
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