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Gaussian Prompter: Linking 2D Prompts for 3D Gaussian Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Pan, Honghan Liu, Bangzhen Xu, Xuemiao Zheng, Chenxi Nie, Yongwei He, Shengfeng South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot China

Interactive 3D segmentation in radiance fields is crucial for advanced 3D scene understanding and manipulation. However, existing methods often struggle to achieve both volumetric completeness and segmentation accuracy, primarily because they fail to consider the critical links between 2D prompt-based segmentations across multiple views. Motivated by this gap, we introduce Gaussian Prompter, a novel approach specifically designed for 3D Gaussian Splatting. The core idea behind Gaussian Prompter is to seamlessly integrate a Gaussian-centric segmentation paradigm by effectively linking various 2D prompts from multi-view segmentations to ensure consistent 3D segmentation. To realize this, we employ two tailored approaches: GaussBlend and PinPrompt. GaussBlend aggregates multi-view 2D segmentation masks into a cohesive 3D segmentation, ensuring both accuracy and completeness. PinPrompt leverages high-confidence prompts from adjacent views to enhance segmentation precision further. Additionally, to address the lack of complex datasets in 3D segmentation, we introduce the SegMip-360 dataset, which includes over 350 precisely annotated masks across seven scenes. Extensive experiments demonstrate that the Gaussian Prompter significantly outperforms state-of-the-art methods in both segmentation accuracy and completeness. Our video demonstrations can be found at our project page. © 1979-2012 IEEE.

关键词： 3D segmentation Radiance fields User-interactive scene manipulation

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning

arXiv

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arXiv 2025年

作者： Liu, Bangzhen Zheng, Chenxi Xu, Xuemiao Xu, Cheng Zhang, Huaidong He, Shengfeng South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology The State Key Laboratory of Subtropical Building and Urban Science The Ministry of Education Key Laboratory of Big Data and Intelligent Robot The Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Singapore Management University Singapore

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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Fast incremental structure from motion based on parallel bundle adjustment

Fast incremental structure from motion based on parallel bun...

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作者： Cao, Mingwei Zheng, Liping Jia, Wei Liu, Xiaoping Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion;namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cameras

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Study on high temperature energy loss mechanism of energy storage polymer composite based on interface structure regulation 4

Study on high temperature energy loss mechanism of energy st...

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4th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2022

作者： Chen, Chen Yan, Shubin Zhang, Tiandong School of Electrical Engineering Zhejiang University of Water Resources and Electric Power Zhejiang-Belarus Jt. Lab. of Intelligent Equip. and Syst. for Water Conserv. and Hydropower Safety Monitoring Hangzhou China College of Electrical & Electronic Engineering Harbin Univ. of Science and Technology Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin University of Science and Technology Harbin China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

With the swiftly development of polymer-based films capacitors, the energy loss or efficiency have fascinated much more focus on practical application. Lower energy storage efficiency means a large amount loss of electrical energy. In this study, the inorganic functional layer AIN is deposited on both sides of the linear dielectric PEN using magnetron sputtering. The results show that the growth of AIN inorganic layer with good insulating properties can lower the energy loss to a certain extent. The AIN/PEN/AIN multilayer composite films obtained energy storage performance at corresponding breakdown strength. The existence of the inorganic layer on the surface can inhibit the introduction of electrode charges, thereby reducing the energy loss. © 2022 IEEE.

关键词： Energy dissipation

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NSPG-Miner: Mining Repetitive Negative Sequential Patterns

arXiv

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arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

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Fourth-moment Method for Reliability Analysis Based on FMKL Generalized Lambda Distribution

Fourth-moment Method for Reliability Analysis Based on FMKL ...

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2021 International Conference on Mechanical, Aerospace and Automotive engineering, CMAAE 2021

作者： Huang, ChangFeng Sheng, XiangQian Fan, WenLiang School of Civil Engineering Chongqing University Chongqing400045 China Key Lab. of New Technology for Construction of Cities in Mountain Area of Ministry of Education Chongqing University Chongqing400045 China

ISBN: (纸本)9781450385206

In practical engineering, because of the randomness of geometric parameters, load parameters, etc., it is more reasonable to treat engineering system as a stochastic system. Reliability analysis is an important part of stochastic system analysis. Cumulative distribution function is a complete description of statistical characteristics of random variables, and moments can also describe some important characteristics of random variables. Moment method has the advantages of no iterative and partial derivative calculation in reliability analysis. Firstly, the Freimer-Mudholkar-Kollia-Lin generalized lambda distribution (FMKL generalized lambda distribution) with first four moment information is introduced, and the approximate expression of the applicable range of the distribution is fitted. By combining FMKL generalized lambda distribution with the adaptive point estimation method based on the bivariate dimensionality reduction approximation of moment, a moment method of reliability analysis is proposed. Finally, several numerical and engineering cases are investigated to verify the easy-implementation, accuracy and wide applicability of the proposed method. © 2021 ACM.

关键词： Adaptive point estimate method FMKL generalized lambda distribution Structural reliability

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Generalized Category Discovery with Large Language Models in the Loop

arXiv

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arXiv 2023年

作者： An, Wenbin Shi, Wenkai Tian, Feng Lin, Haonan Wang, QianYing Wu, Yaqiang Cai, Mingxiang Wang, Luyan Chen, Yan Zhu, Haiping Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China Lenovo Research China University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlab.led data by utilizing a few lab.led data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) 1 into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalab.e Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are availab.e at https://***/Lackel/LOOP. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Effect of Nb Addition on Microstructure and Mechanical Properties of Fe-5.5 Wt% B Alloy

SSRN

引用

SSRN 2025年

作者： Ma, Zhen Yan, Yudong Zhang, Yuanhu Li, Qiang Liu, Qicong Yuan, Shidan Yang, Hui Wu, Mingzhong Zhuang, Minghui School of Materials Science and Engineering Jiamusi University Jiamusi154007 China School of Materials Science and Engineering Key Lab. for Anisotropy and Texture of Materials Education Ministry of China Northeastern University China Beijing100081 China Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing Beijing100083 China

To further improve the toughness of the hypereutectic Fe-B hardfacing alloy, the effect of niobium (Nb) on the microstructure, toughness, and wear resistance of the hypereutectic Fe-B hardfacing alloy was studied. In this study, a series of characterization techniques such as metallographic microscope, X-ray diffraction, field emission scanning electron microscope, electron backscatter diffraction, impact test, bending test, hardness test, and abrasive wear test were used to describe the effect of Nb addition. The results show that the microstructure of Fe-B alloy is mainly composed of Fe2B, NbFeB, and α-Fe after adding the Nb element. With the increase of Nb content, the volume fraction of primary Fe2B gradually decreases, and the volume fraction of NbFeB and α-Fe increases. The eutectic structure transforms from Fe2B + α-Fe to lamellar NbFeB + α-Fe. The toughness of the alloy increases gradually with the increase of Nb content. When the Nb content is 19.7 wt%, the alloy has the highest impact toughness and fracture toughness, which are 30.8 J/cm2 and 21.6 MPa·m1/2, respectively. The fracture mode changes from cleavage fracture to cleavage fracture + ductile fracture, which significantly improves the toughness of the alloy. In addition, the macro hardness and abrasive wear weight loss show a trend of increasing first and then decreasing with the addition of Nb content. When the Nb content is 6.6 wt%, the Rockwell hardness of the alloy is 64.2 HRC, and the minimum wear weight loss under 48 N load is 60.2 mg. Adding Nb is an effective way to improve the toughness of Fe-B alloy. © 2025, The Authors. All rights reserved.

关键词： Iron alloys

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Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization

arXiv

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arXiv 2023年

作者： Cao, Yiwen Su, Yukun Wang, Wenjun Liu, Yanxia Wu, Qingyao The School of Software Engineering South China University of Technology Guangzhou510640 China The Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou China The School of Computer Science and Engineering Nanyang Technological University Singapore The Pazhou Lab Guangzhou China

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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