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3rd International Conference on computer science and Communication technology, ICCSCT 2022

作者： Du, Shanshan Zhang, Xiaomeng Zhu, Shanbang Zhao, Ruiwei Xu, Weidong Feng, Rui School of Information Science and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Department of Orthopedics Changhai Hospital Naval Medical University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (纸本)9781510661240

In some long and untrimmed videos, locating the important and key segments can be a very challenging task for temporal action detection. Current methods make remarkable progress when it comes to RGB images. The aim of this paper is to develop a method with the assistance of dynamic model of human body skeletons to address this problem. To this end, we propose a Feature Pyramid Graph Convolutional Network (FP-GCN). The introduced model contains a Feature Encoding Module to encode skeleton data with graph convolutional networks, a Feature Pyramid Module to exploit the inherent pyramidal hierarchy and an Action Detection Module to generate the final prediction results of the detection. We experiment our approach on NTU RGB+D and THUMOS14 datasets and obtain a satisfactory result. © 2022 SPIE.

关键词： Convolution

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EC-SLAM: Effectively Constrained Neural RGB-D SLAM with Sparse TSDF Encoding and Global Bundle Adjustment

arXiv

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arXiv 2024年

作者： Li, Guanghao Chen, Qi Yan, Yuxiang Pu, Jian Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

We introduce EC-SLAM, a real-time dense RGB-D simultaneous localization and mapping (SLAM) system leveraging Neural Radiance Fields (NeRF). While recent NeRF-based SLAM systems have shown promising results, they have yet to fully exploit NeRF’s potential to constrain pose optimization. EC-SLAM addresses this by using sparse parametric encodings and Truncated Signed Distance Fields (TSDF) to represent the map, enabling efficient fusion, reducing model parameters, and accelerating convergence. Our system also employs a globally constrained Bundle Adjustment (BA) strategy that capitalizes on NeRF’s implicit loop closure correction capability, improving tracking accuracy by reinforcing constraints on keyframes most relevant to the current optimized frame. Furthermore, by integrating a feature-based and uniform sampling strategy that minimizes ineffective constraint points for pose optimization, we reduce the impact of random sampling in NeRF. Extensive evaluations on the Replica, ScanNet, and TUM datasets demonstrate state-of-the-art performance, with precise tracking and reconstruction accuracy achieved alongside real-time operation at up to 21 Hz. The source code is available at https://***/Lightingooo/EC-SLAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Motion Matters: A Novel Motion Modeling for Cross-View Gait Feature Learning

Motion Matters: A Novel Motion Modeling for Cross-View Gait ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingqi Li Jiaqi Gao Yuzhen Zhang Hongming Shan Junping Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone (GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used crossview gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods.

关键词： Representation learning Clothing Buildings Signal processing Network architecture Feature extraction Robustness

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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Cross-Head Supervision for Crowd Counting with Noisy Annotations

Cross-Head Supervision for Crowd Counting with Noisy Annotat...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingliang Dai Zhizhong Huang Jiaqi Gao Hongming Shan Junping Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China

Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc. These noisy annotations severely affect the model training, especially for density map-based methods. To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision. The resultant model, CHS-Net, can synergize different types of inductive biases for better counting. In addition, we develop a progressive cross-head supervision learning strategy to stabilize the training process and provide more reliable supervision. Extensive experimental results on Shang-haiTech and QNRF datasets demonstrate superior performance over state-of-the-art methods. Code is available at https://***/RaccoonDML/CHSNet.

关键词： Training Adaptation models Codes Annotations Convolution Transformers Noise robustness

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DO-FAM: Disentangled Non-Linear Latent Navigation For Facial Attribute Manipulation

DO-FAM: Disentangled Non-Linear Latent Navigation For Facial...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yifan Yuan Siteng Ma Hongming Shan Junping Zhang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China

Facial attribute manipulation (FAM) aims to edit the semantic attributes of facial images according to the user’s requirements. Unfortunately, the majority of existing FAM methods struggle in meeting at least one of the two requirements: high reconstruction quality and high irrelevance preservation. To alleviate these two limitations, we propose a novel Disentangled nOn-linear latent navigation framework for FAM, termed DO-FAM. To promote the reconstruction quality, we leverage hypernetworks to fine-tune a pre-trained StyleGAN2 generator. To decouple entangled attributes, we propose a novel Disentangled nOn-Linear Latent transformation module, named DOLL, which consists of three components: (1) a decomposer to factorize input latent codes into two parts: attribute-related and attribute-unrelated; (2) a non-linear Latent Transformation Network (LTNet) to navigate the attribute-related latent codes to the target one with respect to the designed attribute(s); and (3) a latent classifier tasked with predicting latent codes’ attributes to guide the latent code navigation. Extensive experimental results on a widely-used benchmark facial editing dataset, CelebA-HQ, demonstrate the superiority of our method over state-of-the-art methods.

关键词： Image quality Codes Navigation Semantics Signal processing Generators Task analysis

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Causal Discovery by Continuous Optimization with Conditional Independence Constraint: Methodology and Performance

Causal Discovery by Continuous Optimization with Conditional...

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IEEE International Conference on Data Mining (ICDM)

作者： Yewei Xia Hao Zhang Yixin Ren Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Department of Computer Science & Technology Tongji University China

Discovering causal relationships from observational data is a challenging topic in artificial intelligence. Recent works formulate causal discovery as a continuous optimization problem with a differentiable acyclic constraint. Although these methods have achieved considerable performance improvement, they have two drawbacks: 1) they require a relatively large number of training samples; and 2) their performance will substantially deteriorate when facing heterogeneous noise. To address these problems, we first propose a low-order conditional independence (CI) constraint for the continuous optimization problem, and then design a soft version of the constraint by transforming it to a regularization term in the loss function of the continuous optimization problem. We show the convergence of continuous optimization with our constraint under some mild conditions, and the consistency of causal structure learning with the CI regularization. Extensive experiments on both synthetic and real-world datasets show that with our CI constraint or regularization, existing continuous optimization methods can achieve considerable performance improvement of causal discovery, especially when sample size is small.

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scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

arXiv

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arXiv 2023年

作者： Jiang, Weikang Wang, Jinxian Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Department of Computer Science and Technology Tongji University China

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware Iterative Contrastive Learning (CICL in short) for scRNA-seq data clustering, which utilizes an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CICL consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss module. First, CICL extracts the feature vectors of the original and augmented data by the Transformer-encoder. Then, it computes the clustering centroids by K-means and employs the student’s t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result becomes better and better. Extensive experiments on 25 real-world scRNA-seq datasets show that CICL outperforms the state-of-the-art (SOTA) methods. Concretely, CICL surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively. Source code is available at https://***/Alunethy/CICL. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases 29

LFKQG: A Controlled Generation Framework with Local Fine-tun...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Fei, Zichu Zhou, Xin Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan Unviersity China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

Question generation over knowledge bases (KBQG) aims to generate natural questions about a subgraph that can be answered by a given answer entity. Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph. (2) There are a large number of out-of-vocabulary (OOV) predicates in real-world scenarios, which are hard to adapt for most KBQG models. To address these challenges, we propose LFKQG, a controlled generation framework for Question Generation over Knowledge Bases. (1) LFKQG employs a simple controlled generation method to generate the questions containing the critical entities in the subgraph, ensuring the question is relevant to the whole subgraph. (2) We propose an optimization strategy called local fine-tuning, which makes good use of the rich information hidden in the pre-trained model to improve the ability of the model to adapt the OOV predicates. Extensive experiments show that our method outperforms existing methods greatly on three widely-used benchmark datasets SimpleQuestion, PathQuestions, and WebQuestions . © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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