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The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

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arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China 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 Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML. © 2022, CC BY-NC-SA.

关键词： Embeddings

Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

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arXiv 2022年

作者： Dong, Jianfeng Wang, Yabing Chen, Xianke Qu, Xiaoye Li, Xirong He, Yuan Wang, Xun The College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Electronic Information and Communications Huazhong University of Science and Technology Hubei430074 China The Key Lab of Data Engineering and Knowledge Engineering Renmin University of China The AIMC Lab. School of Information Renmin University of China Beijing100872 China The Alibaba Group Beijing100102 China

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://***/LiJiaBei-7/rivrl. © 2022, CC BY.

关键词： Video recording

CEPV: A Tree Structure Information Extraction and Visualization Tool for Big knowledge Graph

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CEPV: A Tree Structure Information Extraction and Visualizat...

IEEE International Conference on Big knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

DualConvNet: Enhancing CNN Inference Efficiency Through Compressed Convolutions and Reparameterization

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DualConvNet: Enhancing CNN Inference Efficiency Through Comp...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haipeng Du Muyan Jiao Jiageng Zhang Xin Lv Jie Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We present DualConvNet, a novel CNN architecture that enhances inference efficiency through two key innovations: compressed convolutions and reparameterization. Compressed convolutions reduce computational complexity by selectively processing input channel subsets during both training and inference. For inference, we introduce a reparameterization technique that merges the multi-branch structure into a single, efficient operation, significantly improving speed. Experiments on CIFAR-10, CIFAR-100, and ImageNet-1k demonstrate DualConvNet’s effectiveness, consistently outperforming state-of-the-art models in both accuracy and inference speed. On the COCO dataset, DualConvNet shows competitive accuracy in object detection and instance segmentation tasks while substantially reducing GPU latency. Ablation studies validate the impact of our dual-strategy approach, revealing significant improvements in both accuracy and computational efficiency compared to alternative designs. These results demonstrate DualConvNet’s effectiveness in improving inference efficiency while maintaining high accuracy across various tasks and datasets, making it particularly suitable for real-time applications in resource-constrained scenarios.

关键词： Training Technological innovation Accuracy Image coding Computational modeling Computer architecture Transformers Computational efficiency Convolutional neural networks Computational complexity

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

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arXiv 2021年

作者： Fan, Zhaoxin Zhu, Yazhi He, Yulin Sun, Qi Liu, Hongyan He, Jun Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China No. 59 Zhongguancun Street Haidian Dist Beijing100872 China Institute of Information Science Beijing Jiaotong University No.3 Shangyuancun Haidian Dist. Beijing China School of Economics and Management Tsinghua University Haidian Dist Beijing100084 China

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Augmented reality

Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer

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arXiv 2023年

作者： Luo, Anwei Cai, Rizhao Kong, Chenqi Ju, Yakun Kang, Xiangui Huang, Jiwu Kot, Alex C. The School of Information Technology Jiangxi University of Finance and Economics Nanchang330013 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Lab. School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The China-Singapore International Joint Research Institute Singapore

With the rapid progress of generative models, the current challenge in face forgery detection is how to effectively detect realistic manipulated faces from different unseen domains. Though previous studies show that pre-trained Vision Transformer (ViT) based models can achieve some promising results after fully fine-tuning on the Deepfake dataset, their generalization performances are still unsatisfactory. One possible reason is that fully fine-tuned ViT-based models may disrupt the pre-trained features [1], [2] and overfit to some data-specific patterns [3]. To alleviate this issue, we present a Forgery-aware Adaptive Vision Transformer (FA-ViT) under the adaptive learning paradigm, where the parameters in the pre-trained ViT are kept fixed while the designed adaptive modules are optimized to capture forgery features. Specifically, a global adaptive module is designed to model long-range interactions among input tokens, which takes advantage of self-attention mechanism to mine global forgery clues. To further explore essential local forgery clues, a local adaptive module is proposed to expose local inconsistencies by enhancing the local contextual association. In addition, we introduce a fine-grained adaptive learning module that emphasizes the common compact representation of genuine faces through relationship learning in fine-grained pairs, driving these proposed adaptive modules to be aware of fine-grained forgery-aware information. Extensive experiments demonstrate that our FA-ViT achieves state-of-the-arts results in the cross-dataset evaluation, and enhances the robustness against unseen perturbations. Particularly, FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation. The code and trained model have been released at: https://***/LoveSiameseCat/FAViT. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

A Method for Updating Individual Tree Species Samples Based on Different Images

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A Method for Updating Individual Tree Species Samples Based ...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Caiyan Chen Linhai Jing Fulong Chen Hui Li Baoxin Hu Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China International Research Center of Big Data for Sustainable Development Goals Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education & School of Geography and Environment Jiangxi Normal University Nanchang China Department of Earth and Space Science and Engineering York University Toronto Ontario Canada

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The correct classification of individual tree species (ITS) is of great importance for forest resource management. However, current ITS samples collection is time-consuming and laborious in field surveys, and the samples cannot be updated in time. In this study, a sample update method is proposed, which updates samples by detecting the spectral angular similarity of samples at different times at the same location, enabling the reuse of historical samples and timely update of samples. Experimental results showed that the classification accuracy could reach more than 90% by using DenseNet model and the updated samples, which proved that the sample update method could achieve sample update and sample reuse effectively. In addition, there was significant improvement in the classification accuracy of the sample set using multispectral and texture information fusion, proving the effectiveness of information fusion in increasing classification information.

关键词： Surveys Accuracy Laser radar Soft sensors Geoscience and remote sensing Vegetation Forestry

Few-shot deep adversarial learning for video-based person re-identification

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arXiv 2019年

作者： Wu, Lin Wang, Yang Yin, Hongzhi Wang, Meng Shao, Ling Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China University of Queensland St Lucia4072 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei230000 China Abu Dhabi United Arab Emirates

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

New entanglement-assisted quantum codes from negacyclic codes

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arXiv 2023年

作者： Chen, Xiaojing Lu, Xingbo Zhu, Shixin Jiang, Wan Wang, Xindi School of Internet Anhui University Anhui Hefei230039 China School of Mathematics Hefei University of Technology Anhui Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Hefei230601 China School of Computer and Information Hefei University of Technology Anhui Hefei230601 China

The theory of entanglement-assisted quantum error-correcting codes (EAQECCs) is a generalization of the standard stabilizer quantum error-correcting codes, which can be possibly constructed from any classical codes by relaxing the duality condition and utilizing preshared entanglement between the sender and receiver. In this paper, a new family of EAQECCs is constructed from negacyclic codes of length n = q2a+1, where q is an odd prime power, a = m22+1 and m is an odd integer. Some new entanglement-assisted quantum maximum distance separable (EAQMDS) codes are obtained in the sense that their parameters are not covered by the previously known ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement