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SIAM: A Simple Alternating Mixer for Video Prediction

SIAM: A Simple Alternating Mixer for Video Prediction

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xin Zheng Ziang Peng Yuan Cao Hongming Shan Junping Zhang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Department of Aeronautics and Astronautics Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Video prediction, predicting future frames from the previous ones, has broad applications such as autonomous driving and weather forecasting. Existing state-of-the-art methods typically focus on extracting either spatial, temporal, or spatiotemporal features from videos. Different feature focuses, resulting from different network architectures, may make the resultant models excel at some video prediction tasks but perform poorly on others. Towards a more generic video prediction solution, we explicitly model these features in a unified encoder-decoder framework and propose a simple alternating Mixer (SIAM). The novelty of SIAM lies in the design of dimension alternating mixing (DaMi) blocks, which can model spatial, temporal, and spatiotemporal features through alternating the dimensions of the feature maps. Extensive experimental results demonstrate the superior performance of the proposed SIAM on four benchmark video datasets covering both synthetic and real-world scenarios.

关键词： Weather forecasting Predictive models Streaming media Network architecture Benchmark testing Feature extraction Spatiotemporal phenomena

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Learning from Noisy labels with Coarse-to-Fine Sample Credibility Modeling 17th

Learning from Noisy Labels with Coarse-to-Fine Sample Cred...

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17th European Conference on computer Vision, ECCV 2022

作者： Zhang, Boshen Li, Yuxi Tu, Yuanpeng Peng, Jinlong Wang, Yabiao Wu, Cunlin Xiao, Yang Zhao, Cairong YouTu Lab Shenzhen China Tongji University Shanghai China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250620

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well. In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc.) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Investigation of Singing Voice Separation for Singing Voice Detection in Polyphonic Music 9th

Investigation of Singing Voice Separation for Singing Voic...

引用

9th Conference on Sound and Music technology, CSMT 2021

作者： Sun, Yifu Zhang, Xulong Chen, Xi Yu, Yi Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Co. Ltd. Shenzhen518000 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Chiyoda163-8001 Japan Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Singing voice detection (SVD), to recognize vocal parts in the song, is an essential task in music information retrieval (MIR). The task remains challenging since singing voice varies and intertwines with the accompaniment music, especially for some complicated polyphonic music such as choral music recordings. To address this problem, we investigate singing voice detection while discarding the interference from the accompaniment. The proposed SVD has two steps: i. The singing voice separation (SVS) technique is first utilized to filter out the singing voice’s potential part coarsely. ii. Upon the continuity of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN) is used to learn compositional features. Moreover, to eliminate the outliers, we choose to use a median filter for time-domain smoothing. Experimental results show that the proposed method outperforms the existing state-of-the-art works on two public datasets, the Jamendo Corpus and the RWC pop dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Music

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Video Captioning via Relation-Aware Graph Learning

Video Captioning via Relation-Aware Graph Learning

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

作者： Yi Zheng Heming Jing Qiujie Xie Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University China School of Information Management and Engineering Shanghai University of Finance and Economics China School of Information Technology Deakin University Australia

Recent neural models for video captioning usually employed an encoder-decoder framework. However, most approaches either neglected the spatial and temporal interactions between objects in a video or implicitly modelled the interactions, resulting in less desired performance. In this paper, we propose a novel relation-aware graph learning framework. It explicitly models both spatial and temporal relations for objects. In particular, a relation-aware graph is designed to depict the spatial relations between different objects in a scene. Parallelly, a temporal graph network is designed to perform relational reasoning for the same objects in adjacent frames. Features of both types of relations are learned and fused for the follow-up language decoder. Experiments on two bench-mark datasets show the effectiveness of our framework. It achieves state-of-the-art performance with CIDEr scores on MSVD and MSR-VTT.

关键词： Visualization Signal processing Cognition Acoustics Decoding Speech processing

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MFB: A 3D point cloud segmentation model based on Mamba

MFB: A 3D point cloud segmentation model based on Mamba

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computer, Vision and intelligent technology (ICCVIT), International Conference on

作者： Xing Wang Jiaqi Zhang Xin Zhang Lei Liu Jun Wang Weibiao Tan College of Electronic and Information Engineering Hebei University School of Computer Science and Technology Huaibei Normal University Huaibei Key Lab of Digital Multimedia Intelligent Information Processing

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Mamba-based approaches have performed extremely well in point cloud segmentation tasks, but still have limitations regarding computational efficiency and global context understanding. To solve this problem, we propose MFB (Mamba Fusion Block), which composes of two parts, one is similar to the previous Mamba block, and the other is our integrated linear attention and Mamba-like Linear Attention (MLLA). Among them, Linear Attention implements a mechanism based on rotational position encoding for processing point cloud features, which can better obtain the feature details from point cloud data and improve the model’s performance for point cloud segmentation. MLLA, while maintaining the original structure of Mamba Block, further enhances the model’s capacity to learn features at multiple scales. The experimental outcomes indicate that Our proposed approach outshines the existing Mamba-based methods on the ShapeNetPart dataset.

关键词： Point cloud compression Training Geometry Solid modeling computer vision Three-dimensional displays Computational modeling Encoding Data models Convergence

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Weakly-supervised Temporal Action Localization with Adaptive Clustering and Refining Network

Weakly-supervised Temporal Action Localization with Adaptive...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hao Ren Wu Ran Xingson Liu Haoran Ren Hong Lu Rui Zhang Cheng Jin Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Peng Cheng Laboratory Shenzhen China

Weakly-supervised temporal action localization task aims to localize temporal boundaries of action instances by using only video-level labels. Existing methods primarily adopt Multi-Instance-Learning (MIL) scheme to handle this task. The effectiveness of MIL scheme depends heavily on the selection of top-k action snippets, which is unstable and requires manual tuning. To address these deficiencies, we propose an Adaptive Clustering and Refining Network (ACRNet). Specifically, we present an action-aware clustering strategy that is adaptable and requires no manual tuning to separate action and background snippets of diverse videos based on intra-class activation distribution. And a cluster refining step is included to eliminate false action snippets by considering inter-class activation distribution, which greatly improves robustness and localization accuracy. Extensive experiments on THUMOS14, ActivityNet 1.2&1.3 benchmarks show that our method achieves state-of-the-art performance.

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Long-Term Cloth-Changing Person Re-identification 15th

Long-Term Cloth-Changing Person Re-identification

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15th Asian Conference on computer Vision, ACCV 2020

作者： Qian, Xuelin Wang, Wenxuan Zhang, Li Zhu, Fangrui Fu, Yanwei Xiang, Tao Jiang, Yu-Gang Xue, Xiangyang School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China School of Data Science and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China University of Surrey Guildford United Kingdom

ISBN: (纸本)9783030695347

Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent setting, under which a person re-appears in different camera views with the same outfit. A discriminative feature representation learned by existing deep Re-ID models is thus dominated by the visual appearance of clothing. In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is much understudied due to the lack of large scale datasets. The first contribution of this work is a new LTCC dataset containing people captured over a long period of time with frequent clothing changes. As a second contribution, we propose a novel Re-ID method specifically designed to address the cloth-changing challenge. Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable. We, therefore, introduce a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information. Extensive experiments show that superior performance is achieved by the proposed model on the new LTCC dataset. The dataset is available on the project website: https://***/LTCC_Perosn_***. © 2021, Springer Nature Switzerland AG.

关键词： Large dataset

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Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow

Bring Event into RGB and LiDAR: Hierarchical Visual-Motion F...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hanyu Zhou Yi Chang Zhiwei Shi National Key Lab of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Single RGB or LiDAR is the mainstream sensor for the challenging scene flow, which relies heavily on visual features to match motion features. Compared with single modality, existing methods adopt a fusion strategy to directly fuse the cross-modal complementary knowledge in motion space. However, these direct fusion methods may suffer the modality gap due to the visual intrinsic heterogeneous nature between RGB and LiDAR, thus deteriorating motion features. We dis-cover that event has the homogeneous nature with RGB and LiDAR in both visual and motion spaces. In this work, we bring the event as a bridge between RGB and LiDAR, and propose a novel hierarchical visual-motion fusion frame-work for scene flow, which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation. In visual fusion, we discover that event has a complementarity (relative v.s. absolute) in luminance space with RGB for high dynamic imaging, and has a complemen-tarity (local boundary v.s. global shape) in scene structure space with LiDAR for structure integrity. In motion fusion, we figure out that RGB, event and LiDAR are complementary (spatial-dense, temporal-dense v.s. spatiotemporal-sparse) to each other in correlation space, which motivates us to fuse their motion correlations for motion continuity. The proposed hierarchical fusion can explicitly fuse the multimodal knowledge to progressively improve scene flow from visual space to motion space. Extensive experiments have been performed to verify the superiority of the proposed method.

关键词： Bridges Visualization Laser radar Correlation Fuses Shape Imaging

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Fan-Net: Fourier-Based Adaptive Normalization for Cross-Domain Stroke Lesion Segmentation

Fan-Net: Fourier-Based Adaptive Normalization for Cross-Doma...

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

作者： Weiyi Yu Yiming Lei Hongming Shan Institute of Science and Technology for Brain-Inspired Intelligence Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable attention. However, the existing methods often neglect the domain shift among MR images collected from different sites, which has limited performance improvement. To address this problem, we intend to change style information without affecting high-level semantics via adaptively changing the low-frequency amplitude components of the Fourier transform so as to enhance model robustness to varying domains. Thus, we propose a novel FAN-Net, a U-Net–based segmentation network incorporated with a Fourier-based adaptive normalization (FAN) and a domain classifier with a gradient reversal layer. The FAN module is tailored for learning adaptive affine parameters for the amplitude components of different domains, which can dynamically normalize the style information of source images. Then, the domain classifier provides domain-agnostic knowledge to endow FAN with strong domain generalizability. The experimental results on the ATLAS dataset, which consists of MR images from 9 sites, show the superior performance of the proposed FAN-Net compared with baseline methods.

关键词： Image segmentation Fans Semantics Neural networks Magnetic resonance Stroke (medical condition) Signal processing

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MUSICAL TEMPO ESTIMATION USING A MULTI-SCALE NETWORK 22

MUSICAL TEMPO ESTIMATION USING A MULTI-SCALE NETWORK

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22nd International Conference on Music information Retrieval, ISMIR 2021

作者： Sun, Xiaoheng He, Qiqi Gao, Yongwei Li, Wei School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781732729902

Recently, some single-step systems without onset detection have shown their effectiveness in automatic musical tempo estimation. Following the success of these systems, in this paper we propose a Multi-scale Grouped Attention Network to further explore the potential of such methods. A multi-scale structure is introduced as the overall network architecture where information from different scales is aggregated to strengthen contextual feature learning. Furthermore, we propose a Grouped Attention Module as the key component of the network. The proposed module separates the input feature into several groups along the frequency axis, which makes it capable of capturing long-range dependencies from different frequency positions on the spectrogram. In comparison experiments, the results on public datasets show that the proposed model outperforms existing state-of-the-art methods on Accuracy1. © 2021 Proceedings of the 22nd International Conference on Music information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Network architecture

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