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Softmax-Free Linear Transformers Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-13 Jiachen Lu, Junge Zhang, Xiatian Zhu, Jianfeng Feng, Tao Xiang, Li Zhang
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One-Shot Neural Face Reenactment via Finding Directions in GAN’s Latent Space Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-13
Abstract In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head pose/expression disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different approach
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Alignment Relation is What You Need for Diagram Parsing IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-13 Xinyu Zhang, Lingling Zhang, Xin Hu, Jun Liu, Shaowei Wang, Qianying Wang
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A distributed permutation flow-shop considering sustainability criteria and real-time scheduling J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-12 Amir M. Fathollahi-Fard, Lyne Woodward, Ouassima Akhrif
Recent advancements in production scheduling have arisen in response to the need for adaptation in dynamic environments. This paper addresses the challenge of real-time scheduling within the context of sustainable production. We redefine the sustainable distributed permutation flow-shop scheduling problem using an online mixed-integer programming model. The proposed model prioritizes minimizing makespan
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Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-12 Amir Ghasemi, Fatemeh Farajzadeh, Cathal Heavey, John Fowler, Chrissoleon T. Papadopoulos
Production Scheduling (PS) is an essential paradigm within supply and manufacturing systems and an important element of sustainable development. PS, mainly known for its horizontal effects within the operational decision level, directly impacts both tactical and strategical levels of decision-making. In other words, an optimally designed and utilized PS module could bring efficiency towards the whole
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Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Huali Xu, Li Liu, Shuaifeng Zhi, Shaojing Fu, Zhuo Su, Ming-Ming Cheng, Yongxiang Liu
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Part-Object Progressive Refinement Network for Zero-Shot Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Man Liu, Chunjie Zhang, Huihui Bai, Yao Zhao
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YOLOH: You Only Look One Hourglass for Real-time Object Detection. IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Shaobo Wang, Renhai Chen, Hongyue Wu, Xiaozhe Li, Zhiyong Feng
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A Large-Scale Network Construction and Lightweighting Method for Point Cloud Semantic Segmentation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Jiawei Han, Kaiqi Liu, Wei Li, Guangzhi Chen, Wenguang Wang, Feng Zhang
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COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Jingyi Liao, Xun Xu, Manh Cuong Nguyen, Adam Goodge, Chuan Sheng Foo
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Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Dingquan Li, Kede Ma, Jing Wang, Ge Li
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Self-Supervised Monocular Depth Estimation with Positional Shift Depth Variance and Adaptive Disparity Quantization IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Juan Luis Gonzalez Bello, Jaeho Moon, Munchurl Kim
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Layer-Specific Knowledge Distillation for Class Incremental Semantic Segmentation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Qilong Wang, Yiwen Wu, Liu Yang, Wangmeng Zuo, Qinghua Hu
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Semi-supervised 3D Shape Segmentation via Self Refining IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Zhenyu Shu, Teng Wu, Jiajun Shen, Shiqing Xin, Ligang Liu
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Efficient Single Correspondence Voting for Point Cloud Registration IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Xuejun Xing, Zhengda Lu, Yiqun Wang, Jun Xiao
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Weakly-Supervised RGBD Video Object Segmentation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Jinyu Yang, Mingqi Gao, Feng Zheng, Xiantong Zhen, Rongrong Ji, Ling Shao, Aleš Leonardis
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Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Xiaoyun Zheng, Liwei Liao, Jianbo Jiao, Feng Gao, Ronggang Wang
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Comprehensive Attribute Prediction Learning for Person Search by Language IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-12 Kai Niu, Linjiang Huang, Yuzhou Long, Yan Huang, Liang Wang, Yanning Zhang
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Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-12 Shi‐Zhi Chen, De‐Cheng Feng, Ertugrul Taciroglu
Structural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this
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Autonomous flight strategy of an unmanned aerial vehicle with multimodal information for autonomous inspection of overhead transmission facilities Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-12 Munsu Jeon, Joonhyeok Moon, Siheon Jeong, Ki‐Yong Oh
This study proposes an innovative method for achieving autonomous flight to inspect overhead transmission facilities. The proposed method not only integrates multimodal information from novel sensors but also addresses three essential aspects to overcome the existing limitations in autonomous flights of an unmanned aerial vehicle (UAV). First, a novel deep neural network architecture titled the rotational
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PL $${}_{1}$$ P: Point-Line Minimal Problems under Partial Visibility in Three Views Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-10
Abstract We present a complete classification of minimal problems for generic arrangements of points and lines in space observed partially by three calibrated perspective cameras when each line is incident to at most one point. This is a large class of interesting minimal problems that allows missing observations in images due to occlusions and missed detections. There is an infinite number of such
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Deep Learning Technique for Human Parsing: A Survey and Outlook Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-09 Lu Yang, Wenhe Jia, Shan Li, Qing Song
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Emergency Evacuation Based on Long Range Communication Technology J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-11 Xue Jiang, Peihong Zhang, Xinwei Zhang, Aoran Yu, Bang Chen, Chenghao Ye, Jiabao Song
The complexity and uncertainty of a fire in a large public building with complex structure leads to risks during emergency evacuation. Therefore, it is crucial to conduct intelligent emergency evacuation according to the situational development of the accident environment, the crowding degree of evacuation routes, as well as the reliability and flexibility of data transmission in large complex building
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Intelligent Optimization Design of Squeeze Casting Process Parameters Based on Neural Network and Improved Sparrow Search Algorithm J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-11 Jianxin Deng, Guangming Liu, Ling Wang, Gang Liu, Xiusong Wu
Squeeze casting process parameters are the key to squeeze casting production and to obtain excellent performance casts. To realize intelligent optimization design of process parameters under various requirements, this work presents a new intelligent optimization design framework for squeeze casting process parameters based on process data and integrating a two-stage intelligent integrated optimization
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Blockchain and NFT-based traceability and certification for UAV parts in manufacturing J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-11 Diana Hawashin, Mohamed Nemer, Khaled Salah, Raja Jayaraman, Davor Svetinovic, Ernesto Damiani
In recent years, the widespread adoption of Unmanned Aerial Vehicles (UAVs) has increased significantly, sparking the need for reliable mechanisms to verify the authenticity, origin, and history of their constituent components. However, the lack of secure and trusted evidence for traceability, attestation, and certification of these components poses alarming challenges in ensuring transparency, data
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In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-11 Hoofar Shokravi, Mohammadreza Vafaei, Bijan Samali, Norhisham Bakhary
Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality
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Advancing the white phase mobile traffic control paradigm to consider pedestrians Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-11 Ramin Niroumand, Leila Hajibabai, Ali Hajbabaie
Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using
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Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-08 Guofeng Mei, Cristiano Saltori, Elisa Ricci, Nicu Sebe, Qiang Wu, Jian Zhang, Fabio Poiesi
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Adaptive Multi-Source Predictor for Zero-Shot Video Object Segmentation Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-07 Xiaoqi Zhao, Shijie Chang, Youwei Pang, Jiaxing Yang, Lihe Zhang, Huchuan Lu
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NesTD-Net: Deep NESTA-Inspired Unfolding Network With Dual-Path Deblocking Structure for Image Compressive Sensing IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-08 Hongping Gan, Zhen Guo, Feng Liu
Deep compressive sensing (CS) has become a prevalent technique for image acquisition and reconstruction. However, existing deep learning (DL)-based CS methods often encounter challenges such as block artifacts and information loss during iterative reconstruction, particularly at low sampling rates, resulting in a reduction of reconstructed details. To address these issues, we propose NesTD-Net, an
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“What’s Going On” with BizDevOps: A qualitative review of BizDevOps practice Comput. Ind. (IF 10.0) Pub Date : 2024-03-08 Pedro Antunes, Mary Tate
BizDevOps is an emerging trend that seeks to cut back the lag between product/service vision and implementation. However, so far this trend has been mainly unnoticed by research. This paper carries out a “grey literature” (non-academic) review on BizDevOps. Data is collected from reports, articles, webpages, and blog posts to capture the professionals’ insights on BizDevOps. We develop a conceptual
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A multiscale model for wood combustion Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-08 H. L. Hao, R. Y. Qin, C. L. Chow, D. Lau
Understanding wood combustion has become increasingly critical as fire safety engineering moves toward a performance‐based approach to building design. Although different kinetic models have been developed for wood burning, chemical kinetics remains a significant challenge for accurate prediction. This work has developed a novel multiscale model by implementing kinetic parameters calculated from molecular
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Open Set Recognition in Real World Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-07 Zhen Yang, Jun Yue, Pedram Ghamisi, Shiliang Zhang, Jiayi Ma, Leyuan Fang
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Does Confusion Really Hurt Novel Class Discovery? Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-07
Abstract When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is known as novel class discovery (NCD). However, category
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Domain Generalization with Small Data Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-06 Kecheng Chen, Elena Gal, Hong Yan, Haoliang Li
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A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-06 Huan Yin, Xuecheng Xu, Sha Lu, Xieyuanli Chen, Rong Xiong, Shaojie Shen, Cyrill Stachniss, Yue Wang
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Industrial blockchain threshold signatures in federated learning for unified space-air-ground-sea model training J. Ind. Inf. Integr. (IF 15.7) Pub Date : 2024-03-07 Jingxue Chen, Zengxiang Wang, Gautam Srivastava, Turki Ali Alghamdi, Fazlullah Khan, Saru Kumari, Hu Xiong
The space-air-ground-sea three-dimensional (3D) network is a comprehensive communication network system. This 3D network combines extensive coverage of satellite communications, adaptability of unmanned aerial vehicle (UAV) communications, reliability of terrestrial communications, and the necessity for maritime communications. These networks generate enormous amounts of data, and training machine
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CSFwinformer: Cross-Space-Frequency Window Transformer for Mirror Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Zhifeng Xie, Sen Wang, Qiucheng Yu, Xin Tan, Yuan Xie
Mirror detection is a challenging task since mirrors do not possess a consistent visual appearance. Even the Segment Anything Model (SAM), which boasts superior zero-shot performance, cannot accurately detect the position of mirrors. Existing methods determine the position of the mirror under hypothetical conditions, such as the correspondence between objects inside and outside the mirror, and the
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VRT: A Video Restoration Transformer IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc Van Gool
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Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Tianfang Sun, Zhizhong Zhang, Xin Tan, Yanyun Qu, Yuan Xie
Weakly supervised point cloud semantic segmentation methods that require 1% or fewer labels with the aim of realizing almost the same performance as fully supervised approaches have recently attracted extensive research attention. A typical solution in this framework is to use self-training or pseudo-labeling to mine the supervision from the point cloud itself while ignoring the critical information
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HeadDiff: Exploring Rotation Uncertainty With Diffusion Models for Head Pose Estimation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Yaoxing Wang, Hao Liu, Yaowei Feng, Zhendong Li, Xiangjuan Wu, Congcong Zhu
In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process
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PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Kui Zhang, Gang Hua, Yueqiang Cheng, Nenghai Yu
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HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Burak Ercan, Onur Eker, Canberk Saglam, Aykut Erdem, Erkut Erdem
Event-based cameras are becoming increasingly popular for their ability to capture high-speed motion with low latency and high dynamic range. However, generating videos from events remains challenging due to the highly sparse and varying nature of event data. To address this, in this study, we propose HyperE2VID, a dynamic neural network architecture for event-based video reconstruction. Our approach
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Task-Specific Normalization for Continual Learning of Blind Image Quality Models IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Weixia Zhang, Kede Ma, Guangtao Zhai, Xiaokang Yang
In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness. The key step in our approach is to freeze all convolution filters of a pre-trained deep neural network (DNN) for an explicit promise of stability, and learn task-specific normalization
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Mutual Information Driven Equivariant Contrastive Learning for 3D Action Representation Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Lilang Lin, Jiahang Zhang, Jiaying Liu
Self-supervised contrastive learning has proven to be successful for skeleton-based action recognition. For contrastive learning, data transformations are found to fundamentally affect the learned representation quality. However, traditional invariant contrastive learning is detrimental to the performance on the downstream task if the transformation carries important information for the task. In this
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Consensus-Agent Deep Reinforcement Learning for Face Aging IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Ling Lin, Hao Liu, Jinqiao Liang, Zhendong Li, Jiao Feng, Hu Han
Face aging tasks aim to simulate changes in the appearance of faces over time. However, due to the lack of data on different ages under the same identity, existing models are commonly trained using mapping between age groups. This makes it difficult for most existing aging methods to accurately capture the correspondence between individual identities and aging features, leading to generating faces
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Unsupervised Modality-Transferable Video Highlight Detection With Representation Activation Sequence Learning IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Tingtian Li, Zixun Sun, Xinyu Xiao
Identifying highlight moments of raw video materials is crucial for improving the efficiency of editing videos that are pervasive on internet platforms. However, the extensive work of manually labeling footage has created obstacles to applying supervised methods to videos of unseen categories. The absence of an audio modality that contains valuable cues for highlight detection in many videos also makes
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Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-07 Congqi Cao, Yue Lu, Yanning Zhang
Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not
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Thermal contraction coordination behavior between unbound aggregate layer and asphalt mixture overlay based on the finite difference and discrete element coupling method Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-05 Tongtong Wan, Hainian Wang, Xu Yang, Yu Chen, Lian Li, Aboelkasim Diab
The constraint action of the unbound aggregate layer underneath plays an important role in affecting the temperature strains in the top asphalt layer. The focus of the present paper is to investigate the interactive thermal contraction mechanisms between the asphalt mixture and granular base layers to offer a new perspective in promoting the understanding of the thermal cracking disease. In this paper
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CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-05 Xi Zhao, Wei Feng, Zheng Zhang, Jingjing Lv, Xin Zhu, Zhangang Lin, Jinghe Hu, Jingping Shao
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Automated Detection of Cat Facial Landmarks Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-05
Abstract The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks
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Toward Robust Referring Image Segmentation IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-05 Jianzong Wu, Xiangtai Li, Xia Li, Henghui Ding, Yunhai Tong, Dacheng Tao
Referring Image Segmentation (RIS) is a fundamental vision-language task that outputs object masks based on text descriptions. Many works have achieved considerable progress for RIS, including different fusion method designs. In this work, we explore an essential question, “What if the text description is wrong or misleading?” For example, the described objects are not in the image. We term such a
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RGBT Tracking via Challenge-Based Appearance Disentanglement and Interaction IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-05 Lei Liu, Chenglong Li, Yun Xiao, Rui Ruan, Minghao Fan
RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role in representing the target appearance in RGBT tracking. In this paper, we propose a novel approach, which performs target appearance representation disentanglement and interaction via both modality-shared and modality-specific challenge attributes, for robust RGBT tracking
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Exploring Hierarchical Information in Hyperbolic Space for Self-Supervised Image Hashing IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-05 Rukai Wei, Yu Liu, Jingkuan Song, Yanzhao Xie, Ke Zhou
In real-world datasets, visually related images often form clusters, and these clusters can be further grouped into larger categories with more general semantics. These inherent hierarchical structures can help capture the underlying distribution of data, making it easier to learn robust hash codes that lead to better retrieval performance. However, existing methods fail to make use of this hierarchical
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Federated learning–based global road damage detection Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-05 Poonam Kumari Saha, Deeksha Arya, Yoshihide Sekimoto
Deep learning is widely used for road damage detection, but it requires extensive, diverse, and well‐labeled data. Centralized model training can be difficult due to large data transfers, storage needs, and computational resources. Data privacy concerns can also hinder data sharing among clients, leaving them to train models on their own data, leading to less robust models. Federated learning (FL)
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PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition Int. J. Comput. Vis. (IF 19.5) Pub Date : 2024-03-04
Abstract We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across \(\sim \) 20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by
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Sparse Action Tube Detection IEEE Trans. Image Process. (IF 10.6) Pub Date : 2024-03-04 Yixuan Li, Zhenzhi Wang, Zhifeng Li, Limin Wang
Action tube detection is a challenging task as it requires not only to locate action instances in each frame, but also link them in time. Existing action tube detection methods often employ multi-stage pipelines with complex designs and time-consuming linking procedure. In this paper, we present a simple end-to-end action tube detection method, termed as Sparse Tube Detector (STDet). Unlike those dense
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A controllable generative model for generating pavement crack images in complex scenes Comput. Aided Civ. Infrastruct. Eng. (IF 9.6) Pub Date : 2024-03-04 Hancheng Zhang, Zhendong Qian, Wei Zhou, Yitong Min, Pengfei Liu
Existing crack recognition methods based on deep learning often face difficulties when detecting cracks in complex scenes such as brake marks, water marks, and shadows. The inadequate amount of available data can be primarily attributed to this factor. To address this issue, a controllable generative model of pavement cracks is proposed that can generate crack images in complex scenes by leveraging