检索结果-内蒙古大学图书馆

32nd ACM International Conference on Multimedia, MM 2024

作者： Chen, Jiankang Deng, Ling Gan, Zhiyong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Guangdong Guangzhou China China United Network Communications Corporation Limited Guangdong Branch Guangdong Guangzhou China School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangdong Guangzhou China

ISBN: (纸本)9798400706868

Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD detection performance, they are limited due to costly data collection or simplified assumptions. In this paper, we propose a novel OOD detection framework FodFoM that innovatively combines multiple foundation models to generate two types of challenging fake outlier images for classifier training. The first type is based on BLIP-2's image captioning capability, CLIP's vision-language knowledge, and Stable Diffusion's image generation ability. Jointly utilizing these foundation models constructs fake outlier images which are semantically similar to but different from in-distribution (ID) images. For the second type, GroundingDINO's object detection ability is utilized to help construct pure background images by blurring foreground ID objects in ID images. The proposed framework can be flexibly combined with multiple existing OOD detection methods. Extensive empirical evaluations show that image classifiers with the help of constructed fake images can more accurately differentiate real OOD image from ID ones. New state-of-the-art OOD detection performance is achieved on multiple benchmarks. The code is available at https://***/Cverchen/ACMMM2024-FodFoM. © 2024 ACM.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. This cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. Then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Towards Completeness: A Generalizable Action Proposal Generator for Zero-Shot Temporal Action Localization 27th

Towards Completeness: A Generalizable Action Proposal Genera...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Du, Jia-Run Lin, Kun-Yu Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031784439

To address the zero-shot temporal action localization (ZSTAL) task, existing works develop models that are generalizable to detect and classify actions from unseen categories. They typically develop a category-agnostic action detector and combine it with the Contrastive Language-Image Pre-training (CLIP) model to solve ZSTAL. However, these methods suffer from incomplete action proposals generated for unseen categories, since they follow a frame-level prediction paradigm and require hand-crafted post-processing to generate action proposals. To address this problem, in this work, we propose a novel model named Generalizable Action Proposal generator (GAP), which can interface seamlessly with CLIP and generate action proposals in a holistic way. Our GAP is built in a query-based architecture and trained with a proposal-level objective, enabling it to estimate proposal completeness and eliminate the hand-crafted post-processing. Based on this architecture, we propose an Action-aware Discrimination loss to enhance the category-agnostic dynamic information of actions. Besides, we introduce a Static-Dynamic Rectifying module that incorporates the generalizable static information from CLIP to refine the predicted proposals, which improves proposal completeness in a generalizable manner. Our experiments show that our GAP achieves state-of-the-art performance on two challenging ZSTAL benchmarks, i.e., Thumos14 and ActivityNet1.3. Specifically, our model obtains significant performance improvement over previous works on the two benchmarks, i.e., +3.2% and +3.4% average mAP, respectively. The code is available at https://***/Run542968/GAP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Depth-Enhanced Alignment for Label-Free 3D Semantic Segmentation 27th

Depth-Enhanced Alignment for Label-Free 3D Semantic Segment...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Shangjin Feng, Jiawei Chen, Zibo Liu, Zhixuan Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031784552

Labeling every point in a scene is a laborious journey for 3D understanding. To achieve annotation-free training, existing works introduce Contrastive Language-Image Pre-training (CLIP) to transfer the pre-trained capability of visual-linguistic correspondence to 3D-linguistic matching. However, directly adopting this CLIP-driven strategy can inevitably introduce bias: The overrated roles of the color and texture from an RGB image could overshadow the geometric nature of the corresponding 3D scene, resulting in a sub-optimal alignment. We note that different from RGB images, a depth map contains rich geometric information. Inspired by this, we propose Depth-Enhanced Alignment (D-EA) for label-free 3D semantic segmentation. D-EA aims to explore the rich geometric cues in depth maps and mitigate the color and texture biases rooted in the original CLIP-driven strategy. Specifically, we first tune a geometry-enhanced CLIP by aligning its depth prediction to the paired RGB prediction given by the original CLIP. Next, the point cloud feature space is matched with the RGB-Depth aggregated CLIP space by aligning point prediction to RGB and depth predictions. Moreover, to mitigate the semantic ambiguity caused by view-specific noise, we propose a View-Integrated Pseudo Label Generation paradigm. Experiments demonstrate the effectiveness of the proposed D-EA on the ScanNet (indoor) and GraspNet-1Billion (desktop) datasets in the label-free setting. Our method is also competitive in limited annotation semantic segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preserving Face Recognition with Adaptive Generative Perturbations 27th

Privacy-Preserving Face Recognition with Adaptive Generative...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Delong Peng, Yixing Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031783401

For online face recognition services, the potential leakage of facial features and reconstruction techniques gives malicious attackers the opportunity to reconstruct face images, raising public concern about privacy. Previous privacy-preserving face recognition methods either require retraining the face recognition model or iterative perturbation that increases inference time. To overcome these limitations, we propose an efficient plug-and-play method that crafts Adaptive Generative perturbations for frozen Face recognition model (AGFace) to defend the reconstruction attacks. To generate perturbation with a single forward for shallow features extracted by frozen recognition model, we learn a Perturbation Generator to efficiently mine the adversarial perturbations by simultaneously minimizing the perturbation and maximizing the reconstruction error of a proxy reconstructor. To achieve privacy-utility trade-off, we propose the Adaptive Channel Selector to identify top-k reconstruction-sensitive channels for the features. By selecting these channels for perturbation, the dominant visual privacy information is protected with reconstruction-insensitive discriminative information preserved. Extensive experiments demonstrate that AGFace achieves state-of-the-art performance in terms of both privacy and utility among retraining-free methods and is comparable to retraining-dependent methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Optimal scheme for dynamic adjustment of active mirror in FAST system 2021

Optimal scheme for dynamic adjustment of active mirror in FA...

引用

2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Hongchuan Zhou Jiaqi Liu Junyan Song Benchao Yang State Key Laboratory of Mathematical Engineering and advanced computing China

ISBN: (纸本)9781450384155

This paper mainly studies the optimal scheme of dynamic adjustment of active mirror in FAST system by establishing mathematical model. According to the constraints such as distance variation range, a mathematical model describing the dynamic trajectory of the position and angle of the active mirror is established, and the correctness of the solution of the model is verified by simulation. The optimal surface model is selected from all the results. When the measured object is directly above the fast system, it is modeled based on the ideal parabola to ensure that the expansion constraints and threshold conditions of the actuator are met. Because the distribution of actuator and cable in the whole fast system is not a uniform sphere, the change of radial distance of cable can be considered as the expansion of actuator; In addition, the model is optimized from two aspects: adjusting the position of focus on the horizontal plane and changing the size of zoom alignment. From these two aspects, the parabolic focusing range satisfying the expansion range is obtained. Finally, the optimization strategy of dynamic adjustment of active mirror in fast system is given.

关键词： Dimension reduction to simplify mathematical modeling Optimization model

来源：评论

学校读者我要写书评

暂无评论

A Survey on Knowledge Graph Representation Learning with Text Information 21

A Survey on Knowledge Graph Representation Learning with Tex...

引用

Proceedings of the 3rd World Symposium on Software engineering

作者： Ling Wang Jicang Lu Gang Zhou State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450384094

Knowledge graph representation learning provides a lot of help for subsequent tasks such as knowledge graph completion, information retrieval, and intelligent question answering. By representing the knowledge graph as low-dimensional dense vectors, these tasks can improve efficiency significantly. However, limited by the sparseness and huge scale of the actual structure, representation learning only from a structural perspective can no longer meet the research *** improve the performance, researchers introduced auxiliary information into representation learning. This paper focuses on models using text as auxiliary information, dividing all text-combined models into two categories: Closed-world assumption models and Open-world assumption models. The former is limited by the model's demand for the structural representation of the graph itself, and can only predict the entities and relationships that already exist in the knowledge graph. The latter can handle entities that have not previously been seen during model training, and connect brand-new entities to the knowledge graph, which is more in line with the dynamic trend of the knowledge graph in real world. Open-world assumption models can be further subdivided into multiple types according different joint functions, such as alignment function, fusion function, score function and transformation function. This paper summarizes existing methods in detail and looks forward to future possible research directions.

关键词： Open-world assumption Representation learning Knowledge graph Text information

来源：评论

学校读者我要写书评

暂无评论

Coarse-to-Fine Proposal Refinement Framework for Audio Temporal Forgery Detection and Localization 24

Coarse-to-Fine Proposal Refinement Framework for Audio Tempo...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wu, Junyan Lu, Wei Luo, Xiangyang Yang, Rui Wang, Qian Cao, Xiaochun School of Computer Science and Engineering Sun Yat-sen University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Alibaba Group Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

ISBN: (纸本)9798400706868

Recently, a novel form of audio partial forgery has posed challenges to its forensics, requiring advanced countermeasures to detect subtle forgery manipulations within long-duration audio. However, existing countermeasures still serve a classification purpose and fail to perform meaningful analysis of the start and end timestamps of partial forgery segments. To address this challenge, we introduce a novel coarse-to-fine proposal refinement framework (CFPRF) that incorporates a frame-level detection network (FDN) and a proposal refinement network (PRN) for audio temporal forgery detection and localization. Specifically, the FDN aims to mine informative inconsistency cues between real and fake frames to obtain discriminative features that are beneficial for roughly indicating forgery regions. The PRN is responsible for predicting confidence scores and regression offsets to refine the coarse-grained proposals derived from the FDN. To learn robust discriminative features, we devise a difference-aware feature learning (DAFL) module guided by contrastive representation learning to enlarge the sensitive differences between different frames induced by minor manipulations. We further design a boundary-aware feature enhancement (BAFE) module to capture the contextual information of multiple transition boundaries and guide the interaction between boundary information and temporal features via a cross-attention mechanism. Extensive experiments show that our CFPRF achieves state-of-the-art performance on various datasets, including LAV-DF, ASVS2019PS, and HAD. © 2024 ACM.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Graph Prompt Clustering

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5794-5805页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning530219 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Beijing100816 China South China Technology Commercialization Center Guangzhou510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510033 China Ministry of Education Beijing100044 China

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification 8

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

引用

8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Xiaohua Yang, Jie Hu, Min Wu, Xia Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis. © 2023 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：