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5th International Conference on Intelligent Control, Measurement and Signal processing, ICMSP 2023

作者： Zou, Liangji Tan, Yihua School of Artificial Intelligence and Automation Huazhong University of Science and Technology National Key Lab of Multispectral Information Intelligent Processing Technology Wuhan China

ISBN: (纸本)9798350336030

In a multi-agent environment, efficient collab.rative search among multiple unmanned aerial vehicles (UAVs) is crucial for area search and path planning. Therefore, it is important to consider the collab.rative learning among UAVs when designing collab.rative strategies. This paper analyzes the problems of existing value-decomposition-based multi-agent reinforcement learning algorithms in multi-UAV area search and path planning, namely the underestimation of optimal joint actions, which leads to suboptimal policy generation. Subsequently, a new non-monotonicity value-decomposition algorithm is proposed based on the addition of a masked highway connection strategy. This algorithm suppresses the optimization of certain action pairs and focuses more on the optimal joint actions, thus better optimizing the objective and recovering the value of optimal joint actions. Multiple simulation experiments demonstrate that the proposed algorithm can achieve improved performance in environments involving UAV swarms and enhance the collab.rative effectiveness of UAV swarms in search tasks. © 2023 IEEE.

关键词： Motion planning

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Cross-video Identity Correlating for Person Re-identification Pre-training 38

Cross-video Identity Correlating for Person Re-identificatio...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Zuo, Jialong Nie, Ying Zhou, Hanyu Zhang, Huaxin Wang, Haoyu Guo, Tianyu Sang, Nong Gao, Changxin National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Huawei Noah's Ark Lab Canada

Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-identification. However, these researches are mostly confined to pre-training at the instance-level or single-video tracklet-level. They ignore the identity-invariance in images of the same person across different videos, which is a key focus in person re-identification. To address this issue, we propose a Cross-video Identity-cOrrelating pre-traiNing (CION) framework. Defining a noise concept that comprehensively considers both intra-identity consistency and inter-identity discrimination, CION seeks the identity correlation from cross-video images by modeling it as a progressive multi-level denoising problem. Furthermore, an identity-guided self-distillation loss is proposed to implement better large-scale pre-training by mining the identity-invariance within person images. We conduct extensive experiments to verify the superiority of our CION in terms of efficiency and performance. CION achieves significantly leading performance with even fewer training samples. For example, compared with the previous state-of-the-art [9], CION with the same ResNet50-IBN achieves higher mAP of 93.3% and 74.3% on Market1501 and MSMT17, while only utilizing 8% training samples. Finally, with CION demonstrating superior model-agnostic ability, we contribute a model zoo named ReIDZoo to meet diverse research and application needs in this field. It contains a series of CION pre-trained models with spanning structures and parameters, totaling 32 models with 10 different structures, including GhostNet, ConvNext, RepViT, FastViT and so on. © 2024 Neural information processing systems foundation. All rights reserved.

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EXPLORING THE COMMON APPEARANCE-BOUNDARY ADAPTATION FOR NIGHTTIME OPTICAL FLOW 12

EXPLORING THE COMMON APPEARANCE-BOUNDARY ADAPTATION FOR NIGH...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhou, Hanyu Chang, Yi Liu, Haoyue Yan, Wending Duan, Yuxing Shi, Zhiwei Yan, Luxin National Key Lab of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Huawei International Co. Ltd. China

We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus causing invalid motion feature matching. Typically, existing methods employ domain adaptation to transfer knowledge from auxiliary domain to nighttime domain in either input visual space or output motion space. However, this direct adaptation is ineffective, since there exists a large domain gap due to the intrinsic heterogeneous nature of the feature representations between auxiliary and nighttime domains. To overcome this issue, we explore a common-latent space as the intermediate bridge to reinforce the feature alignment between auxiliary and nighttime domains. In this work, we exploit two auxiliary daytime and event domains, and propose a novel common appearance-boundary adaptation framework for nighttime optical flow. In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space. We discover that motion distributions of the two reflectance maps are very similar, benefiting us to consistently transfer motion appearance knowledge from daytime to nighttime domain. In boundary adaptation, we theoretically derive the motion correlation formula between nighttime image and accumulated events within a spatiotemporal gradient-aligned common space. We figure out that the correlation of the two spatiotemporal gradient maps shares significant discrepancy, benefitting us to contrastively transfer boundary knowledge from event to nighttime domain. Moreover, appearance adaptation and boundary adaptation are complementary to each other, since they could jointly transfer global motion and local boundary knowledge to the nighttime domain. Extensive experiments have been performed to verify the superiority of the proposed method. © 2024 12th International Conference on Learning Representations,

关键词： Optical flows

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LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising

LED: A Large-scale Real-world Paired Dataset for Event Camer...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuxing Duan National Key Lab of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Event camera has significant advantages in capturing dynamic scene information while being prone to noise in-terference, particularly in challenging conditions like low threshold and low illumination. However, most existing research focuses on gentle situations, hindering event camera applications in realistic complex scenarios. To tackle this limitation and advance the field, we construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams and showing three notable distinctions compared to others: diverse noise levels and scenes, larger-scale with high-resolution, and high-quality GT. Specifically, it contains stepped parameters and varying illumination with diverse scenarios. Moreover, based on the property of noise events inconsistency and signal events consistency, we propose a novel effective denoising frame-work(DED) using homogeneous dual events to generate the GT with better separating noise from the raw. Further-more, we design a bio-inspired baseline leveraging Leaky-Integrate-and-Fire (LIF) neurons with dynamic thresholds to realize accurate denoising. The experimental results demonstrate that the remarkable performance of the proposed approach on different datasets. The dataset and code are at https://***/Yee-Sing/led.

关键词： Computer vision Noise reduction Noise Neurons Lighting Light emitting diodes Cameras

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Long-range Turbulence Mitigation: A Large-scale Dataset and A Coarse-to-fine Framework

arXiv

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arXiv 2024年

作者： Xu, Shengqi Sun, Run Chang, Yi Cao, Shuning Xiao, Xueyao Yan, Luxin National Key Lab of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology China

Long-range imaging inevitably suffers from atmospheric turbulence with severe geometric distortions due to random refraction of light. The further the distance, the more severe the disturbance. Despite existing research has achieved great progress in tackling short-range turbulence, there is less attention paid to long-range turbulence with significant distortions. To address this dilemma and advance the field, we construct a large-scale real long-range atmospheric turbulence dataset (RLR-AT), including 1500 turbulence sequences spanning distances from 1 Km to 13 Km. The advantages of RLR-AT compared to existing ones: turbulence with longer-distances and higher-diversity, scenes with greater-variety and larger-scale. Moreover, most existing work adopts either registration-based or decomposition-based methods to address distortions through one-step mitigation. However, they fail to effectively handle long-range turbulence due to its significant pixel displacements. In this work, we propose a coarse-to-fine framework to handle severe distortions, which cooperates dynamic turbulence and static background priors (CDSP). On the one hand, we discover the pixel motion statistical prior of turbulence, and propose a frequency-aware reference frame for better large-scale distortion registration, greatly reducing the burden of refinement. On the other hand, we take advantage of the static prior of background, and propose a subspace-based low-rank tensor refinement model to eliminate the misalignments inevitably left by registration while well preserving details. The dynamic and static priors complement to each other, facilitating us to progressively mitigate long-range turbulence with severe distortions. Extensive experiments demonstrate that the proposed method outperforms SOTA methods on different datasets. © 2024, CC BY.

关键词： Pixels

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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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Cross-video identity correlating for person re-identification pre-training 24

Cross-video identity correlating for person re-identificatio...

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Proceedings of the 38th International Conference on Neural information processing Systems

作者： Jialong Zuo Ying Nie Hanyu Zhou Huaxin Zhang Haoyu Wang Tianyu Guo Nong Sang Changxin Gao National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Huawei Noah's Ark Lab.

ISBN: (纸本)9798331314385

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1st Solution Places for CVPR 2023 UG2+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence

arXiv

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arXiv 2023年

作者： Xu, Shengqi Cao, Shuning Liu, Haoyue Xiao, Xueyao Chang, Yi Yan, Luxin National Key Lab of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology China

In this technical report, we briefly introduce the solution of our team "VIElab.HUST" for coded target restoration through atmospheric turbulence in CVPR 2023 UG2+ Track 2.2. In this task, we propose an efficient multi-stage framework to restore a high quality image from distorted frames. Specifically, each distorted frame is initially aligned using image registration to suppress geometric distortion. We subsequently select the sharpest set of registered frames by employing a frame selection approach based on image sharpness, and average them to produce an image that is largely free of geometric distortion, albeit with blurriness. A learning-based deblurring method is then applied to remove the residual blur in the averaged image. Finally, post-processing techniques are utilized to further enhance the quality of the output image. Our framework is capable of handling different kinds of coded target dataset provided in the final testing phase, and ranked 1st on the final leaderboard. Our code will be availab.e at https://***/xsqhust/Turbulence_Removal. © 2023, CC BY-SA.

关键词： Restoration

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

arXiv

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arXiv 2024年

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

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 discover 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 framework 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 complementarity (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. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Incomplete intuitionistic fuzzy behavioral group decision-making based on multigranulation probabilistic rough sets and MULTIMOORA for water quality inspection

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Journal of Intelligent and Fuzzy Systems 2023年第3期44卷 4537-4556页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar School of Computer and Information Technology Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs' psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studie

关键词： Water quality

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