检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Zhou, Pengfei Min, Weiqing Song, Jiajun Zhang, Yang Jiang, Shuqiang The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China The Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou215124 China

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://***/LanceZPF/KEFS. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

ETS: An Error Tolerable System for Coreference Resolution 15

ETS: An Error Tolerable System for Coreference Resolution

引用

15th Conference on Computational Natural Language Learning, CoNLL 2011

作者： Xiong, Hao Song, Linfeng Meng, Fandong Liu, Yang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284084

This paper presents our error tolerable system for coreference resolution in CoNLL-2011(Pradhan et al., 2011) shared task (closed track). Different from most previous reported work, we detect mention candidates based on packed forest instead of single parse tree, and we use beam search algorithm based on the Bell Tree to create entities. Experimental results show that our methods achieve promising results on the development set. © 2011 Association for Computational Linguistics

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

A framework for Chinese event semantic constraint predicates and their generation using PEFT LLM and RAG

引用

International Journal of Machine Learning and Cybernetics 2025年

作者： Huang, Qiaojuan Wang, Shi He, Qing Cao, Cungen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Event semantic analysis is a crucial area of research in natural language processing. It focuses on deeply understanding the semantics of events and their components. While existing semantic frameworks and event semantic labelling cover a wealth of semantic information, further research is needed to explore the fine-grained semantic relationships across participants in different events. To address this problem, we construct a head–tail event semantic constraint predicate framework that relates the complex semantics between different event components. However, considering the large-scale data, an automated method for generating constraint predicates is essential. Therefore, we combine parameter-efficient fine-tuning (PEFT) of large language models (LLMs) with retrieval-augmented generation (RAG) technologies. Specifically, this paper makes four significant contributions. Firstly, we construct a detailed framework of head–tail events semantic constraint predicates by combining manual definitions with the GPT-4o model. The framework includes 117 types of constraint predicates between head–tail events. Secondly, based on this framework, we annotate a dataset of head–tail events semantic constraint predicates and apply it to PEFT LLMs. Thirdly, we use PEFT LLMs prediction error data to construct an event knowledge base covering event entities, semantic roles, and entity types. Fourth, using the event knowledge base, the PEFT LLMs integrate the dual RAG techniques of keywords and vectors to improve the accuracy of generating the constraint predicates. Experimental results show that the method outperforms the baselines, achieving a precision of 97.18% and recall of 97.39%. This research fills the gap in semantic relationships between components across events and provides a novel approach for complex semantic generation tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Exploring the tidal effect of urban business district with large-scale human mobility data

引用

Frontiers of Computer Science 2023年第3期17卷 77-90页

作者： Hongting NIU Ying SUN Hengshu ZHU Cong GENG Jiuchun YANG Hui XIONG Bo LANG School of Computer Science and Engineering Beihang UniversityBeijing 100191China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100086China Baidu Talent Intelligence Center Baidu Inc.Beijing 100085China Business School Imperial College LondonLondon SW72AZUK Artificial Intelligence Thrust The Hong Kong University of Science and TechnologyGuangzhou 510030China

Business districts are urban areas that have various functions for gathering people,such as work,consumption,leisure and *** to the dynamic nature of business activities,there exists significant tidal effect on the boundary and functionality of business ***,effectively analyzing the tidal patterns of business districts can benefit the economic and social development of a ***,with the implicit and complex nature of business district evolution,it is non-trivial for existing works to support the fine-grained and timely analysis on the tidal effect of business *** this end,we propose a data-driven and multi-dimensional framework for dynamic business district ***,we use the large-scale human trajectory data in urban areas to dynamically detect and forecast the boundary changes of business districts in different time ***,we detect and forecast the functional changes in business *** results on real-world trajectory data clearly demonstrate the effectiveness of our framework on detecting and predicting the boundary and functionality change of business ***,the analysis on practical business districts shows that our method can discover meaningful patterns and provide interesting insights into the dynamics of business *** example,the major functions of business districts will significantly change in different time periods in a day and the rate and magnitude of boundaries varies with the functional distribution of business districts.

关键词： business district trajectory functionality detection tidal effect boundary detection visiting score

来源：评论

学校读者我要写书评

暂无评论

CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding

arXiv

引用

arXiv 2024年

作者： Beňová, Ivana Gregor, Michal Gatt, Albert Faculty of Information Technology Brno University of Technology Brno Czech Republic Kempelen Institute of Intelligent Technologies Bratislava Slovakia Utrecht University Department of Information and Computing Sciences Netherlands

This study investigates the ability of various vision-language (VL) models to ground context-dependent and non-context-dependent verb phrases. To do that, we introduce the CV-Probes dataset, designed explicitly for studying context understanding, containing image-caption pairs with context-dependent verbs (e.g., "beg") and non-context-dependent verbs (e.g., "sit"). We employ the MM-SHAP evaluation to assess the contribution of verb tokens towards model predictions. Our results indicate that VL models struggle to ground context-dependent verb phrases effectively. These findings highlight the challenges in training VL models to integrate context accurately, suggesting a need for improved methodologies in VL model training and evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model 12th

Predicting Calibrated Conversion Rate of Online Advertising...

引用

12th CCF Conference on BigData, BigData 2024

作者： Zhang, Xinyue Guo, Yuyao Ao, Xiang School of Information Engineering China University of Geosciences Beijing China Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9789819610235

Accurately predicting conversion rate (CVR) is paramount in online advertising. However, traditional models may face problems such as delayed feedback, where there is a delay of an indeterminate amount of time between click and conversion. Calibration is an effective way to optimize conversion rate estimates in online advertising. Unlike conversion delays, post-click user behaviors occur rapidly and are informative to conversion rate prediction. Our proposed solution, the Multi-Task Mixture-of-Experts Calibration (MTMEC) framework, integrates multi-task learning and mixture-of-experts models. It modifies CVR prediction using post-click user behavior data, utilizing streaming learning for real-time data access. Each task is weighted by a gating network, enabling adaptive loss functions through multi-task learning. Parametric scaling further minimizes calibration errors, enhancing prediction accuracy without excessive parameters. Experiments on real-world datasets validate the effectiveness of MTMEC. It improves model prediction and reduces calibration errors. This framework offers a robust solution for online advertising, bridging the gap between calibration accuracy and system responsiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Data-free Multi-label Image Recognition via LLM-powered Prompt Tuning

arXiv

引用

arXiv 2024年

作者： Yang, Shuo Shang, Zirui Wang, Yongqi Deng, Derong Chen, Hongwei Cheng, Qiyuan Wu, Xinxiao Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China

This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pre-trained Vision-Language Model (VLM) like CLIP to multi-label classification. Through asking LLM by well-designed questions, we acquire comprehensive knowledge about characteristics and contexts of objects, which provides valuable text descriptions for learning prompts. Then we propose a hierarchical prompt learning method by taking the multi-label dependency into consideration, wherein a subset of category-specific prompt tokens are shared when the corresponding objects exhibit similar attributes or are more likely to co-occur. Benefiting from the remarkable alignment between visual and linguistic semantics of CLIP, the hierarchical prompts learned from text descriptions are applied to perform classification of images during inference. Our framework presents a new way to explore the synergies between multiple pre-trained models for novel category recognition. Extensive experiments on three public datasets (MS-COCO, VOC2007, and NUS-WIDE) demonstrate that our method achieves better results than the state-of-the-art methods, especially outperforming the zero-shot multi-label recognition methods by 4.7% in mAP on MS-COCO. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Residual Hyperbolic Graph Convolution Networks

arXiv

引用

arXiv 2024年

作者： Xue, Yangkai Dai, Jindou Lu, Zhipeng Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, we propose residual hyperbolic graph convolutional networks (R-HGCNs) to address the over-smoothing problem. We introduce a hyperbolic residual connection function to overcome the over-smoothing problem, and also theoretically prove the effectiveness of the hyperbolic residual function. Moreover, we use product manifolds and HyperDrop to facilitate the R-HGCNs. The distinctive features of the R-HGCNs are as follows: (1) The hyperbolic residual connection preserves the initial node information in each layer and adds a hyperbolic identity mapping to prevent node features from being indistinguishable. (2) Product manifolds in R-HGCNs have been set up with different origin points in different components to facilitate the extraction of feature information from a wider range of perspectives, which enhances the representing capability of R-HGCNs. (3) HyperDrop adds multiplicative Gaussian noise into hyperbolic representations, such that perturbations can be added to alleviate the over-fitting problem without deconstructing the hyperbolic geometry. Experiment results demonstrate the effectiveness of R-HGCNs under various graph convolution layers and different structures of product manifolds. © 2024, CC BY.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Video Summarization using Denoising Diffusion Probabilistic Model

arXiv

引用

arXiv 2024年

作者： Shang, Zirui Zhu, Yubo Li, Hongxi Yang, Shuo Wu, Xinxiao Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Video summarization aims to eliminate visual redundancy while retaining key parts of video to construct concise and comprehensive synopses. Most existing methods use discriminative models to predict the importance scores of video frames. However, these methods are susceptible to annotation inconsistency caused by the inherent subjectivity of different annotators when annotating the same video. In this paper, we introduce a generative framework for video summarization that learns how to generate summaries from a probability distribution perspective, effectively reducing the interference of subjective annotation noise. Specifically, we propose a novel diffusion summarization method based on the Denoising Diffusion Probabilistic Model (DDPM), which learns the probability distribution of training data through noise prediction, and generates summaries by iterative denoising. Our method is more resistant to subjective annotation noise, and is less prone to overfitting the training data than discriminative methods, with strong generalization ability. Moreover, to facilitate training DDPM with limited data, we employ an unsupervised video summarization model to implement the earlier denoising process. Extensive experiments on various datasets (TVSum, SumMe, and FPVSum) demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

引用

38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

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：