The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Open accessAbstract
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“To improve detection with segmentation, the simplest way is to think of the segmentation network as a fixed feature extractor and to integrate it into a detector as auxiliary features [83, 192, 193].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [83]
The cited source says what the claim attributes to it.
In the citing paper
“Moreover, our method does not exploit many popular improvements, such as iterative regression [9], hard negative mining [35], context modeling [16], stronger data augmentation [22], etc .”
From Feature Pyramid Networks for Object Detection (2017) · cites this paper as [9]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Through this methodology, connections among words can be discerned sans reliance on dictionaries or parsers (Gidaris and Komodakis 2015).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Gidaris and Komodakis 2015
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Cited by
Sorted by citations
Items 701–800 of 803
Correlation-based ConvNet for Small Object Detection in Videos
Bosquet, B., Mucientes, M., & Brea, V. M. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413127 · Cited by 1
Clinical Skin Disease Detection and Classification: Ensembled VGG
Gogineni, S., Suryanarayana, G., Dhanrajnath, P., & Kumar, G. V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-7234-0_78 · Cited by 1
PupilFace: A Cascaded Face Detection and Location Network Fusing Attention
Li, X., & Zou, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89370-5_32 · Cited by 1
ScanCloud: Holistic GPR Data Analysis for Adaptive Subsurface Object Detection
Omwenga, M. M., Wu, D., Yu, L., Huston, D. R., & Xia, T. (2021) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri51335.2021.00027 · Cited by 1
An Edge-Cloud Collaborative Object Detection System
Xu, L., & Yang, D. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-0468-4_28 · Cited by 1
A Study on Indirect Performance Parameters of Object Detection
Arulprakash, E., Martín, A., & Lakshmi, T. M. (2022) · SN Computer Science · doi:10.1007/s42979-022-01277-9 · Cited by 2
Evaluation of Generic Deep Learning Building Blocks for Segmentation of Nineteenth Century Documents
Segal, E., Spencer-Smith, J. B., & Schmidt, D. C. (2023) · Artificial intelligence · doi:10.5772/intechopen.113110 · Cited by 1
Feature Selective Networks for Object DetectionPreprint
Zhai, Y., Fu, J., Lu, Y., & Li, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08879 · Cited by 1
StuffNet: Using 'Stuff' to Improve Object DetectionPreprint
Brahmbhatt, S., Christensen, H. I., & Hays, J. H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05861 · Cited by 1
PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object DetectionPreprint
Tang, Q., Liu, S., Li, J., & Hu, Y. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03295 · Cited by 2
PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization AccuracyPreprint
Xiao, L., Luo, Y., Luo, C., Zhao, L., Fu, Q., Yang, G., Huang, A., & Zhao, Y. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04541 · Cited by 1
Spatio-temporal Tubelet Feature Aggregation and Object Linking in VideosPreprint
Cores, D., Brea, V. M., & Mucientes, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00451 · Cited by 1
G-RCN: Optimizing the Gap between Classification and Localization Tasks for Object DetectionPreprint
Luo, Y., & Xiao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03677 · Cited by 2
Patel, C., Vyas, S., Saikia, P., kalariya, D., & parmar, N. (2022) · doi:10.36227/techrxiv.19642977 · Cited by 2
Object detection with convolutional context features
Kaya, E. C., & Alatan, A. A. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960502
Heterogeneous Image Stylization Using Neural Networks
Bai, T., & Overett, G. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227439
Gated additive skip context connection for object detection
Li, H., Yao, H., Hou, Y., & Sun, X. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296367
The performance of corrected learning network for object recognition
Luo, X., Deng, J., Shen, R., Zhou, Q., Zhang, J., & Zhang, K. (2017) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2017.8359888
Non-local RoIs for Instance SegmentationPreprint
Tseng, S.-Y. R., Chen, H.-T., Tai, S.-H., & Liu, T.-L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.05361
Pedestrian Detection with a Directly-Cascaded Deconvolution-Convolution Structure
Chen, Z., Han, X., Lin, W., Cheng, M., Liu, G., & Xiong, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00776-8_34
Robot Machine Vision by Deep Machine Learning
Lu, H. (2019) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/iteee2019/28812
Learning Diversified Features for Object Detection via Multi-region Occlusion Example Generating
Liang, J., Li, Z., & Guo, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16145-3_42
A joint mechanism algorithm for object detection
Han, Z., Wang, D., Men, A., & Zhou, Y. (2019) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2524207
DropRegion Training of Inception Font Network for High-Performance Chinese Font RecognitionPreprint
Zhong, S. H. Z., Jin, L., Zhang, S., & Wang, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05870
Cascade Region Proposal Networks for Object Detection in the Wild
Yang, D., & Zou, Y. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00300
OptiBox: Breaking the Limits of Proposals for Visual GroundingPreprint
Fan, Z., Meng, S. Y., Sigal, L., & Little, J. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00076
МОДЕЛЬ КАСКАДУ ГІБРИДНОЇ СЕГМЕНТАЦІЇ ДЛЯ АВТОМАТИЧНОГО ДЕШИФРУВАННЯ ОБ’ЄКТІВ НА АЕРОЗНІМКАХ
Kovbasiuk, S., Kanevskyy, L., & Romanchuk, M. P. (2019) · Сучасні інформаційні технології у сфері безпеки та оборони · doi:10.33099/2311-7249/2019-35-2-65-70
Feature Combination Based on Receptive Fields and Cross-Fusion Feature Pyramid for Object Detection
Zhao, Y., Rao, Y., Dong, S., & Jiangnan, Q. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36711-4_4
Lee, K., Ko, J.-G., & Yoo, W. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939591 · Cited by 1
Cascade Feature Pyramid Neural Nwtwork for Object Detector
Han, F., Zhu, Y. M., & Huang, J. (2019) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei48845.2019.8965876
Edge Assisted Object Detection for Mobile Application
Cheng, W., Sun, Q., & Zhang, Y. (2019) · International Conference on Information Technology: IoT and Smart City (ICIT) · doi:10.1145/3377170.3377276
A Scalable Network for Tiny Object Detection Based on Faster RCNN
Li, Y., Ding, Y., Bai, W., Jiao, S., & Pan, Z. (2018) · International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) · doi:10.1109/imccc.2018.00100
Selected and refined local attention module for object detection
Luo, X., & Hu, H. (2020) · Electronics Letters · doi:10.1049/el.2020.0182
Delving into the Imbalance of Positive Proposals in Two-stage Object DetectionPreprint
Ge, Z., Jie, Z., Huang, X., Li, C., & Yoshie, O. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11472
Ventral-Dorsal Neural Networks: Object Detection via Selective AttentionPreprint
Ebrahimpour, M. K., Li, J., Yu, Y.-Y., Reese, J. L., Moghtaderi, A., Yang, M., & Noelle, D. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09727
Deep learning for real-world object detection
Wu, X. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)
R-PFN: Towards Precise Object Detection by Recurrent Pyramidal Feature Fusion
Jia, Q., Wei, S., & Zhao, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60633-6_47
Interpreting and Comparing Convolutional Neural Networks: A Quantitative ApproachPreprint
Islam, M. M., & Tushar, Z. H. (2021) · Preprints.org · doi:10.20944/preprints202101.0579.v1
F3N: Full Feature Fusion Network for Object Detection
Wang, G., Tang, K., & Ouchi, K. (2020) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3446132.3446152
Single-Shot Detection Based on Cyclic Attention
Hu, K., Xu, D., & Kan, J. (2021) · IEEE Access · doi:10.1109/access.2021.3068533
A Proficient Model for Action Detection Using Deep Belief Networks
Krishna, K. S. S. N., & Jesudoss, A. G. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8685-9_55
Research on Classification of UAV Optical Image Tree Species Based on Res2Net
Chen, X., Wu, X.-M., & Wang, S. (2020) · International Conference on Digital Medicine and Image Processing (DMIP) · doi:10.1145/3441369.3441382
Deep neural networks based visual object detection in videos
Jin, R. (2020) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/144136
Enhanced Vote Network for 3D Object Detection in Point Clouds
Zhong, M., & Zeng, G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412216
Intelligent Robust Base-Station Research in Harsh Outdoor Wilderness Environments for Wildsense
Ahn, J., Mysore, A., Zybko, K., Krumm, C. E., Lee, D., Kim, D., Han, R. Y., Mishra, S., & Hobbs, N. T. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.03.001
Real-Time Object Detection based on Divergent Activation
Liang, H., Wang, Y., Ma, Y., & Liu, K. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/914/1/012022
Yu, L. (2017) · SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)
Research on the Robotic Aiming System with Image Recognition and Deep Learning
Ling, X. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-81007-8_74
Cognitive GPR for subsurface sensing based on edge computing and deep reinforcement learning
Omwenga, M. M. (2021) · UTC Scholar (University of Tennessee at Chattanooga)
Developing a mobile manipulation system to handle unknown and unstructured objects
Al-Shanoon, A. A. S. (2021) · e-scholar@UOIT (University of Ontario Institute of Technology)
A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images
Ga, D.-H., Oh, S.-T., & Lim, J.-H. (2021) · Sensors · doi:10.3390/s21227766
Approach to Object Hardness Prediction by Rubber Ball Hardness Prediction Using Capsule Network
Shindo, S., Goto, T., & Tsuchida, K. (2021) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v9i4.3275
Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling
Pototzky, D., Kirschner, M., & Schmidt-Thieme, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92659-5_28
Object detection based on SURF feature matching and Hough transform
Lu, S., Gu, J., Wu, L., & Jiao, L. (2022) · Asia Pacific Conference on Optics Manufacture and International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM) · doi:10.1117/12.2616352
Image Decluttering Techniques and Its Impact on YOLOv4 Performance
Asghari, M., & Alizadeh-Shabdiz, F. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-17292-2_3
Towards Predicting the Life of an Engine
Soni, J. (2023) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-6684-6937-8.ch023
Big Data Helps for Non-Pharmacological Disease Control Measures of COVID-19
Zhao, P., Ren, Y., & Chen, X. (2023) · IGI Global eBooks · doi:10.4018/978-1-7998-9220-5.ch009
End-to-End Object Detection with Location-Sensitive Cues
Wang, C., Zhang, X., Li, Q., Ma, Y., Shi, J. F., & Wang, X. (2022) · International Conference on Network and Information Systems for Computers (ICNISC) · doi:10.1109/icnisc57059.2022.00018
Object Detection Model Based on Dual Visual Pathway
Chen, R., Xiao, Y., & Zeng, Y. (2022) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp55476.2022.10039432
Salient Knowledge-Based Object Detection
Zhang, X., Wang, C., Du, H., Li, Q., Shi, J. F., & Ma, Y. (2022) · International Conference on Control and Robotics (ICCR) · doi:10.1109/iccr55715.2022.10053899
Research on Low-Light Enhancement of Eye Image Dataset Based on DLN
Deng, D. (2023) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2023.060214
Detection of Salient Objects in a Video using a Hybrid Neural Network Model
Indirani, M., Anitha, C., Goswami, S., Baranitharan, K., Govindaraju, S., & Mahaveerakannan, R. (2023) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais56108.2023.10073838
Tree-Based Convolutional Neural Networks for Image Classification
Ansari, A., Raees, S., & Rahman, N. (2023) · International Conference ICT for Digital, Smart and Sustainable Development · doi:10.4108/eai.24-3-2022.2318997
Framework design of reconfigurable convolution neural network acceleration system for edge computing
Liao, Y., Wang, Y., Zhao, Y., Zhang, C., Xiao, B., Yang, J., Wang, H., & Li, J. (2023) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1117/12.2684680
Disentangling Ensemble Models on Adversarial Generalization in Image Classification
Li, C., Pan, M., Yang, B., & Zhang, H. (2023) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei59236.2023.10212535
PGN: A High Quality Proposal Generation Network for Object Detection
Wang, Y., Qu, F., Wang, X., Xu, Y., & Li, Z. (2023) · International Conference on Frontiers of Electronics, Information and Computation Technologies · doi:10.1109/icfeict59519.2023.00100
Jia, L., Li, G., Lu, M., Wei, X., & Yi, G. (2023) · Electronics · doi:10.3390/electronics12183747
GARUDA: Third Eye for Detecting and Tracking Drones
Selvi, S. S., Pavithraa, S., Gupta, I., Awasthi, P., & Kesari, A. K. (2023) · IEEE International Conference on Data, Decision and Systems (ICDDS) · doi:10.1109/icdds59137.2023.10434890
Yabuki, N., Fukuda, T., & Izutsu, R. (2023) · Proceedings e report · doi:10.36253/10.36253/979-12-215-0289-3.111
B., A., S., K., S., M., & M. S., K. (2024) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20230709000793
Xiao, Y., Chen, R., Zhu, H., Zhang, C., & Gu, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4849481
IAFPN: interlayer enhancement and multilayer fusion network for object detection
Li, Z., Yang, C., & Jiang, L. W. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01577-5
WAVELET-POOLING-BASED MULTI-SCALE CNN FOR COVID-19 DETECTION FROM CT IMAGES
Singh, A., Gopi, V. P., & Thomas, A. (2024) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237224500352
Focusing Propagation Pyramid Network for Small Object Detection in Unmanned Aerial Vehicle Images
Yi, Y., Gao, W., Li, L., Li, H., & Xie, J. F. (2024) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs63458.2024.10704398
Evaluation of Classifiers Based on Neural Network for Handwritten Digit Recognition
Sehta, N., Saraf, K., & Vinchi, A. (2024) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset62108.2024.10743433
Group-Based Recursive Learning for Multistage Object Detection
Rao, B. N. K., Chakravarthi, S. S., Ranjana, R., & Nagendra, P. (2024) · Advanced technologies and societal change · doi:10.1007/978-981-99-2832-3_83
RelFormer: Advancing contextual relations for transformer-based dense captioning
Jin, W., Qu, M., Shi, C., Zhao, Y., & Wei, Y. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104300
Road vehicle object detection based on improved YOLOv8
Zhao, Z. (2024) · International Signal Processing, Communications and Engineering Management Conference (ISPCEM) · doi:10.1109/ispcem64498.2024.00074
A YOLO-based Method for Detecting Bone-Irregular in Chest X-rays
Shang, S. (2024) · International Conference on Health Big Data and Intelligent Healthcare (ICHIH) · doi:10.1109/ichih63459.2024.11064826
Discriminative Features Learning Based Approach for Object Detection Enhancement
Ahmad, T., Ullah, A., GenQing, B., Zhang, F., & Ahmad, B. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-92517-7_11
Xu, D., Hu, R., Lu, H., Zeng, G., & Liang, C. (2025) · International Conference on Artificial Intelligence Technologies and Applications (ICAITA) · doi:10.1109/icaita67588.2025.11137846
VIGIA-E: Density-Aware Patch Selection for Edge-Based Small Object Detection with PTZ Cameras
Suárez–Ramírez, J., Sánchez-Cordero, K., & Monzón, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04968-1_23
A Study on a Deep Learning-Based Long-Term Bad Driving Behavior Recognition
Dai, F., Chen, J., & Wang, H. (2025) · International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) · doi:10.1109/iciibms66230.2025.11316769
HDRSeg-UDA: Semantic Segmentation for HDR Images with Unsupervised Domain Adaptation
Lin, H., & Chen, M.-Y. (2026) · Smart Cities · doi:10.3390/smartcities9010010
Usmani, H., Ansari, M. A., & Yadav, R. K. (2025) · International Conference on Advancement in Electronics & Communication Engineering (AECE) · doi:10.1109/aece67531.2025.11386630
Intrinsic motivation mecanisms for incremental learning of visual saliency
Craye, C. (2017) · doi:10.70675/922227cfzcecaz436azb9fez23e8903dbf49 · Cited by 1
Improving speed and image quality of image-based rendering
Ortiz-Cayon, R. (2017) · theses.fr (ABES) · doi:10.70675/3b810877z7f4cz4904z997fzc39bd0cb5569
Conception d'architectures profondes pour l'interprétation de données visuelles
Mordan, T. (2018) · theses.fr (ABES) · doi:10.70675/7263c670z2517z402dzad8fz1f991ba5d034
Annotation-efficient learning for object discovery and detection
Vo, H. V. (2022) · theses.fr (ABES) · doi:10.70675/aae8cd95z8e92z4d35z9384zafe56efdad87
Comparative Analysis of YOLOv10, YOLOv11, and YOLOv12 for Retail Self-Checkout Systems
Vardhan, B. S., Yadav, G. K., Murali, K. H., & Tej, N. S. N. (2026) · International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci67830.2026.11469618
Dual feature masking stage-wise knowledge distillation for object detection
Zhang, Z., Li, J., Wu, Z., Shen, J., & Xu, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114955
Adaptive transformer-augmented network for multi-type defect detection in ball grid array assemblies
Zhiqiang, 焦. J., Zhang, Y., Wang, M., Qiao, S., & Huang, Z. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02856-w · Cited by 1
An Interpretable and Efficient KNN-Based Approach for Object Segmentation
Saadati, S., & Sepahvand, A. (2025) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis68676.2025.11551697
Dianjiang, Z., Liu, Z., Ma, L., & Yuan, H. (2026) · International Conference on Image, Signal Processing and Pattern Recognition (ISPP) · doi:10.1109/ispp69262.2026.11542919
Efficient Channel Transformer for Processing Agricultural Aerial Images
Lu, G., Lin, S., Zhang, S., Jiang, J., Han, Y., Wen, G., & Qiu, T. (2026) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010056
Metadata
- Authors
- Gidaris, S., & Komodakis, N.
- Affiliations
- École nationale des ponts et chaussées (FR)
- ORCID
- Gidaris, Spyros
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 1134-1142
- OpenAlex
- W1932624639
- Cited by
- 801
- References
- 35
- Citations by year
- 2026: 12 · 2025: 33 · 2024: 42 · 2023: 54 · 2022: 63 · 2021: 107 · 2020: 121 · 2019: 126 · 2018: 103 · 2017: 88 · 2016: 45 · 2015: 7
- FWCI
- 33.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (publishedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-13-CORD-0003)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, iterative localization, appearance features, VOC2007
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review