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image classification, image reconstruction,
image representation, image resolution,
object density map estimation
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IP(28), No. 2, February 2019, pp. 1035-1044.
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Kernel, Nonhomogeneous media, Task analysis, Feature extraction,
Quantization (signal), Convolutional neural networks,
count-level classification
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Elsevier DOI
2305
Apply it to the in-filed Rice Panicles Counting.
Occlusion, Object detection, Feature completing, Generative adversarial networks
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Xu, W.[Wei],
Liang, D.K.[Ding-Kang],
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Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object
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2108
Annotations, Feature extraction, Task analysis, Convolution,
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2202
Visualization, Genomics, Bioinformatics, Image segmentation,
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2202
Kernel, Estimation, Feature extraction, Generators, Task analysis,
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2206
Object counting, Variational analysis,
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Global Multi-Scale Information Fusion for Multi-Class Object Counting
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2208
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RS(14), No. 24, 2022, pp. xx-yy.
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McCarthy, T.[Tadhg],
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2305
Class-agnostic counting, Extreme points, Segmentation masks
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Soliven, A.F.O.[Adrienne Francesca O.],
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ConCoNet: Class-agnostic counting with positive and negative
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2306
Object counting, Class-agnostic, Few-shot learning
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IJCV(131), No. 7, July 2023, pp. 1722-1740.
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2307
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Earlier: A1, A2, A3, A4, A6, A5:
From Open Set to Closed Set:
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Code, Counting.
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Gillert, A.[Alexander],
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2309
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Chen, Y.H.[Yue-Hai],
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IP(32), 2023, pp. 6359-6372.
IEEE DOI
2311
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Guo, X.Y.[Xiang-Yu],
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Jiang, S.Q.[Sheng-Qin],
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WWW Link.
2402
Task analysis, Training, Knowledge engineering, Feature extraction,
Deformable models, Convolutional neural networks, Annotations,
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Zhang, Z.R.[Zhen-Rong],
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Ma, J.F.[Jie-Feng],
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WWW Link.
2403
Table structure recognition, Table separation line detection,
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Chen, H.[Hao],
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Gao, B.B.[Bin-Bin],
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CSTrans: Correlation-guided Self-Activation Transformer for Counting
Everything,
PR(153), 2024, pp. 110556.
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WWW Link.
2405
Few-shot counting, Local dependency, Counting everything, Vision transformer
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Tabernik, D.[Domen],
Muhovic, J.[Jon],
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Dense center-direction regression for object counting and
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WWW Link.
2405
Point-supervision, Object counting, Object localization,
Center-point prediction, Center-direction regression, CeDiRNet
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Wang, M.J.[Ming-Jie],
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GCNet: Probing self-similarity learning for Generalized Counting
Network,
PR(153), 2024, pp. 110513.
Elsevier DOI
2405
Class-agnostic counting, Exemplar-free scheme,
Zero-shot paradigm, Self-similarity learning
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Shi, Z.L.[Zeng-Lin],
Mettes, P.[Pascal],
Snoek, C.G.M.[Cees G. M.],
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IJCV(132), No. 7, July 2024, pp. Pages2600-2617.
Springer DOI
2406
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Zhang, S.H.[Shi-Hui],
Huang, Z.G.[Zhi-Gang],
Zhan, S.[Sheng],
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Cui, Z.G.[Zhi-Guo],
Li, F.Y.[Fei-Yu],
Innovative multi-stage matching for counting anything,
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2412
Few-shot counting, Multi-stage matching, Object counting,
Feature matching, Exemplar attention match
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F2CENet: Single-Image Object Counting Based on Block Co-Saliency
Density Map Estimation,
CirSysVideo(34), No. 12, December 2024, pp. 13141-13151.
IEEE DOI
2501
Estimation, Correlation, Streams, Feature extraction, Training,
Accuracy, Convolutional neural networks, Object counting,
density map estimation
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D'Alessandro, A.[Adriano],
Mahdavi-Amiri, A.[Ali],
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Afreeca: Annotation-free Counting for All,
ECCV24(IV: 75-91).
Springer DOI
2412
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Zhu, H.L.[Hui-Lin],
Yuan, J.L.[Jing-Ling],
Yang, Z.W.[Zheng-Wei],
Guo, Y.[Yu],
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Zhong, X.[Xian],
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Zero-shot Object Counting with Good Exemplars,
ECCV24(V: 368-385).
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2412
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Hobley, M.[Michael],
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Abc Easy as 123: A Blind Counter for Exemplar-free Multi-class
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ECCV24(XI: 304-319).
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2412
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Zou, Y.[Yuda],
Xiao, X.[Xin],
Zhou, P.L.[Pei-Lin],
Sun, Z.C.[Zhi-Chao],
Du, B.[Bo],
Xu, Y.C.[Yong-Chao],
Shifted Autoencoders for Point Annotation Restoration in Object
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ECCV24(XXV: 113-130).
Springer DOI
2412
BibRef
Huang, Z.Z.[Zhi-Zhong],
Dai, M.L.[Ming-Liang],
Zhang, Y.[Yi],
Zhang, J.P.[Jun-Ping],
Shan, H.M.[Hong-Ming],
Point, Segment and Count: A Generalized Framework for Object Counting,
CVPR24(17067-17076)
IEEE DOI
2410
Location awareness, Image segmentation, Accuracy, Costs,
Power capacitors, Object detection, Counting, CLIP
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Pelhan, J.[Jer],
Lukežic, A.[Alan],
Zavrtanik, V.[Vitjan],
Kristan, M.[Matej],
DAVE: A Detect-and-Verify Paradigm for Low-Shot Counting,
CVPR24(23293-23302)
IEEE DOI
2410
Accuracy, Codes, Estimation, Benchmark testing, Human in the loop,
object counting, few shot learning,
prompt-based counting
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Zhang, C.[Chenhui],
Wang, S.[Sherrie],
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth
observation data,
EarthVision24(7839-7849)
IEEE DOI
2410
Location awareness, Earth, Visualization, Satellites,
Natural languages, Training data, Land surface, foundation model,
benchmark
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d'Alessandro, A.C.[Adriano C.],
Mahdavi-Amiri, A.[Ali],
Hamarneh, G.[Ghassan],
Learning-to-Count by Learning-to-Rank,
CRV23(105-112)
IEEE DOI
2406
Heating systems, Costs, Codes, Annotations, Estimation,
Benchmark testing, Generative adversarial networks,
Ranking
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Shi, Z.L.[Zeng-Lin],
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Zhang, M.[Mengmi],
Training-free Object Counting with Prompts,
WACV24(322-330)
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2404
Training, Image segmentation, Codes, Annotations, Training data,
Data collection, Algorithms, Image recognition and understanding
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Audebert, N.[Nicolas],
Crucianu, M.[Michel],
Borgne, H.L.[Hervé Le],
Semantic Generative Augmentations for Few-Shot Counting,
WACV24(5431-5440)
IEEE DOI
2404
Training, Measurement, Adaptation models, Semantic segmentation,
Semantics, Object detection, Data models, Algorithms,
Image recognition and understanding
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Đukic, N.[Nikola],
Lukežic, A.[Alan],
Zavrtanik, V.[Vitjan],
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A Low-Shot Object Counting Network With Iterative Prototype
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ICCV23(18826-18835)
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WWW Link.
2401
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Han, T.[Tao],
Bai, L.[Lei],
Liu, L.B.[Ling-Bo],
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STEERER: Resolving Scale Variations for Counting and Localization via
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ICCV23(21791-21802)
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2401
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Huang, Y.F.[Yi-Feng],
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Interactive Class-Agnostic Object Counting,
ICCV23(22255-22265)
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2401
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Araújo, F.[Felipe],
Gadelha, I.[Igor],
Tsukahara, R.[Rodrigo],
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Costa, F.[Filipe],
Vaz, I.[Igor],
Santos, A.[Andreza],
Folego, G.[Guilherme],
Hinting Pipeline and Multivariate Regression CNN for Maize Kernel
Counting on the Ear,
ICIP23(1110-1114)
IEEE DOI
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Sun, G.[Guolei],
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Liu, Y.[Yun],
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Sakaridis, C.[Christos],
Fan, D.P.[Deng-Ping],
Van Gool, L.J.[Luc J.],
Indiscernible Object Counting in Underwater Scenes,
CVPR23(13791-13801)
IEEE DOI
2309
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Xu, J.Y.[Jing-Yi],
Le, H.[Hieu],
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Ranjan, V.[Viresh],
Samaras, D.[Dimitris],
Zero-Shot Object Counting,
CVPR23(15548-15557)
IEEE DOI
2309
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Zgaren, A.[Ahmed],
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Bouguila, N.[Nizar],
Hammoud, R.I.[Riad I.],
MoundCount: A detection-based approach for automatic counting of
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PBVS23(497-506)
IEEE DOI
2309
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Wu, C.S.[Chen-Shen],
van de Weijer, J.[Joost],
Density Map Distillation for Incremental Object Counting,
CLVision23(2506-2515)
IEEE DOI
2309
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Ranjan, V.[Viresh],
Nguyen, M.H.[Minh Hoai],
Exemplar Free Class Agnostic Counting,
ACCV22(IV:71-87).
Springer DOI
2307
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Jenkins, P.[Porter],
Armstrong, K.[Kyle],
Nelson, S.[Stephen],
Gotad, S.[Siddhesh],
Jenkins, J.S.[J. Stockton],
Wilkey, W.[Wade],
Watts, T.[Tanner],
CountNet3D: A 3D Computer Vision Approach to Infer Counts of Occluded
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WACV23(3007-3016)
IEEE DOI
2302
Point cloud compression, Location awareness, Detectors,
Object detection, Inventory management, visual reasoning
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You, Z.Y.[Zhi-Yuan],
Yang, K.[Kai],
Luo, W.H.[Wen-Han],
Lu, X.[Xin],
Cui, L.[Lei],
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Few-shot Object Counting with Similarity-Aware Feature Enhancement,
WACV23(6304-6313)
IEEE DOI
2302
Training, Image recognition, Target recognition, Focusing,
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Li, L.M.[Li-Ming],
Song, S.[Sanming],
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Ye, L.[Lei],
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Feature Evaluation for Underwater Acoustic Object Counting and F0
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ICRVC22(180-185)
IEEE DOI
2301
Shafts, Time-frequency analysis, Source separation,
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Discrete-Constrained Regression for Local Counting Models,
ECCV22(XXIV:621-636).
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2211
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Few-Shot Object Counting and Detection,
ECCV22(XX:348-365).
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2211
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Gong, S.J.[Shen-Jian],
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Class-Agnostic Object Counting Robust to Intraclass Diversity,
ECCV22(XXXIII:388-403).
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2211
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Ranjan, V.[Viresh],
Hoai, M.[Minh],
Vicinal Counting Networks,
L3D-IVU22(4220-4229)
IEEE DOI
2210
Training, Visualization, Buildings, Training data, Generators
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Han, T.[Tao],
Bai, L.[Lei],
Gao, J.Y.[Jun-Yu],
Wang, Q.[Qi],
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DR.VIC: Decomposition and Reasoning for Video Individual Counting,
CVPR22(3073-3082)
IEEE DOI
2210
Codes, Annotations, Estimation, Manuals, Cognition,
Video analysis and understanding,
Scene analysis and understanding
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Shi, M.[Min],
Lu, H.[Hao],
Feng, C.[Chen],
Liu, C.X.[Cheng-Xin],
Cao, Z.G.[Zhi-Guo],
Represent, Compare, and Learn:
A Similarity-Aware Framework for Class-Agnostic Counting,
CVPR22(9519-9528)
IEEE DOI
2210
Visualization, Computational modeling, Pipelines,
Feature extraction, Robustness, Power capacitors, retrieval
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Cheng, Z.Q.[Zhi-Qi],
Dai, Q.[Qi],
Li, H.[Hong],
Song, J.K.[Jing-Kuan],
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Hauptmann, A.G.[Alexander G.],
Rethinking Spatial Invariance of Convolutional Networks for Object
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CVPR22(19606-19616)
IEEE DOI
2210
Convolution, Annotations, Benchmark testing, Feature extraction,
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Michel, A.[Andreas],
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Schenkel, F.[Fabian],
Middelmann, W.[Wolfgang],
Class-aware Object Counting,
RWSurvil22(469-478)
IEEE DOI
2202
Conferences, Estimation, Object detection, Detectors
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Huberman-Spiegelglas, I.[Inbar],
Fattal, R.[Raanan],
Single Image Object Counting and Localizing using Active-Learning,
WACV22(3717-3726)
IEEE DOI
2202
Training, Manifolds, Location awareness, Visualization, Surveillance,
Microscopy, Lighting,
Semi- and Un- supervised Learning
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Yang, S.D.[Shuo-Diao],
Su, H.T.[Hung-Ting],
Hsu, W.H.[Winston H.],
Chen, W.C.[Wen-Chin],
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WACV21(869-877)
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2106
Training, Computational modeling,
Force, Data collection, Data models
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Chen, F.[Feng],
Pound, M.P.[Michael P.],
French, A.P.[Andrew P.],
Learning to Localise and Count with Incomplete Dot-Annotations,
ILDAV21(1612-1620)
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2112
Training, Heating systems, Head, Annotations, Training data,
Semisupervised learning, Fatigue
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Giachetti, A.[Andrea],
Cristani, M.[Marco],
SIMCO: SIMilarity-based object COunting,
ICPR21(47-52)
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2105
Training, Head, Shape, Image color analysis, Annotations,
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LOOC: Localize Overlapping Objects with Count Supervision,
ICIP20(2316-2320)
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Proposals, Training, Games, Task analysis, Object recognition,
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Distortion, Training, Feature extraction, Estimation, Convolution,
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Counting With Focus for Free,
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Code, Counting.
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Towards Locally Consistent Object Counting with Constrained Multi-stage
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End-to-End Instance Segmentation with Recurrent Attention,
CVPR17(293-301)
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1711
Computational modeling, Convolution,
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Chattopadhyay, P.,
Vedantam, R.,
Selvaraju, R.R.,
Batra, D.,
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Counting Everyday Objects in Everyday Scenes,
CVPR17(4428-4437)
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Detectors, Feature extraction, Knowledge discovery,
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Ridder, G.M.,
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An improved watershed algorithm for counting objects in noisy,
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Segment Anything Model .