Describing Textures in the Wild

ImageNet classification with deep convolutional neural networks

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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

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Locality-constrained Linear Coding for image classification

Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714

Aggregating local descriptors into a compact image representation

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Rethinking Self-Supervised Learning: Small is BeautifulPreprint

Cao, Y.-H., & Wu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13559 · Cited by 11

Distributional Prototype Learning for Out-of-distribution Detection

Peng, B., Lü, J., Zhang, Y., Zhang, G., & Fang, Z. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709294 · Cited by 10

Toward Few-Shot Learning in the Open World: A Review and Beyond

Xue, H., An, Y., Qin, Y., Li, W., Wu, Y., Che, Y., Fang, P., & Zhang, M.-L. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3594686 · Cited by 15

On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach

Tong, B., Lai, H., Pan, Y., & Yin, J. P. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01855 · Cited by 12

A Taxonomy of Part and Attribute Discovery Techniques

Maji, S. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-50077-5_10 · Cited by 10

RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection

Khalid, U., Esmaeili, A., Karim, N., & Rahnavard, N. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00028 · Cited by 14

Two-stream coupling network with bidirectional interaction between structure and texture for image inpainting

Shao, X., Ye, H., Yang, B., & Cao, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120700 · Cited by 10

Improving Normalizing Flows with the Approximate Mass for Out-of-Distribution Detection

Chali, S., Kucher, I., Duranton, M., & Klein, J. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00082 · Cited by 10

Cross-domain few-shot learning based on feature adaptive distillation

Zhang, D., Yan, H., Chen, Y., Li, D., & Hao, C. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09318-x · Cited by 10

When an extra rejection class meets out-of-distribution detection in long-tailed image classification

Feng, S., & Wang, C. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106485 · Cited by 10

Dual-View Data Hallucination With Semantic Relation Guidance for Few-Shot Image Recognition

Wu, H., Ye, G., Zhou, Z., Tian, L., Wang, Q., & Lin, L. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3453055 · Cited by 10

Language-Driven Anchors for Zero-Shot Adversarial Robustness

Li, X., Zhang, W., Liu, Y., Hu, Z., Zhang, B., & Hu, X. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02331 · Cited by 10

A Noisy Elephant in the Room: Is Your out-of-Distribution Detector Robust to Label Noise?

Humblot-Renaux, G., Escalera, S., & Moeslund, T. B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02135 · Cited by 13

Towards Latent Masked Image Modeling for Self-supervised Visual Representation Learning

Wei, Y., Gupta, A., & Morgado, P. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_1 · Cited by 10

Enhancing few-shot lifelong learning through fusion of cross-domain knowledge

Zheng, Y., Zhang, X., Tian, Z., & Du, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102730 · Cited by 11

Enhancing Few-Shot Out-of-Distribution Detection With Pre-Trained Model Features

Dong, J., Yao, Y., Wei, J., Zhou, H., Gao, Y., & Fang, Z. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3468874 · Cited by 13

Class Balance Matters to Active Class-Incremental Learning

Huang, Z., Chen, Z., Li, Y., Dong, B., Zhou, E., Liu, Y., Goh, R. S. M., Feng, C.-M., & Zuo, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680822 · Cited by 12

GIST: Improving Parameter Efficient Fine-Tuning via Knowledge Interaction

Ruan, J., Gao, J., Xie, M., Xiang, S., Yu, Z., Liu, T., Fu, Y., & Qu, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680843 · Cited by 15

Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models

Yu, Y.-C., Huang, C.-P., Chen, J. C., Chang, K.-P., Lai, Y.-H., Yang, F.-E., & Wang, Y.-C. F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_13 · Cited by 12

DataDream: Few-Shot Guided Dataset Generation

Kim, J. M., Bader, J., Alaniz, S., Schmid, C., & Akata, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73209-6_15 · Cited by 10

LAPT: Label-Driven Automated Prompt Tuning for OOD Detection with Vision-Language Models

Zhang, Y., Zhu, W., He, C., & Zhang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73220-1_16 · Cited by 16

Self-Supervised Autoencoders for Visual Anomaly Detection

Bauer, A., Nakajima, S., & Müller, K. (2024) · Mathematics · doi:10.3390/math12243988 · Cited by 11

Hard-Normal Example-Aware Template Mutual Matching for Industrial Anomaly Detection

Chen, Z., Xie, X., Yang, L., & Lai, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02323-0 · Cited by 12

Hyperbolic Insights With Knowledge Distillation for Cross-Domain Few-Shot Learning

Yang, X., Kong, D., Wang, N., & Gao, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3551647 · Cited by 11

Active Learning for Vision-Language Models

Safaei, B., & Patel, V. M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00480 · Cited by 11

DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models

Ali, E., Silva, S., & Khan, M. H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00593 · Cited by 13

CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring

Fu, H., Patel, N., Krishnamurthy, P., & Khorrami, F. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00522 · Cited by 20

Selective Visual Prompting in Vision Mamba

Yao, Y., Liu, Z., Cui, Z., Peng, Y., & Zhou, J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i21.34361 · Cited by 11

Fully-Synthetic Training for Visual Quality Inspection in Automotive Production

Huber, C., Knoll, D., & Guthe, M. (2025) · Procedia CIRP · doi:10.1016/j.procir.2025.02.205 · Cited by 14

O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models

Sharifdeen, A., Munir, M. A., Baliah, S., Khan, S. A., & Khan, M. H. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01857 · Cited by 13

Bayesian Test-Time Adaptation for Vision-Language Models

Zhou, L., Ye, M., Li, S., Li, N., Zhu, X., Deng, L., Liu, H., & Lei, Z. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02792 · Cited by 20

Automatic Microstructure Defect Detection of Ti-6Al-4V Titanium Alloy by Regions-Based Graph

Ren, R., Hung, T., & Tan, K. C. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2669523 · Cited by 10

Recognizing Material of a Covered Object: A Case Study With Graffiti

Alfarrarjeh, A., Trivedi, D., Kim, S. H., Park, H., Huang, C., & Shahabi, C. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803286 · Cited by 10

Image retrieval based on texture using latent space representation of discrete Fourier transformed maps

Saikia, S., Fernández-Robles, L., Alegre, E., & Fidalgo, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05955-2 · Cited by 9

Worst Case Matters for Few-Shot Recognition

Fu, M., Cao, Y.-H., & Wu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20044-1_6 · Cited by 10

Bio-Inspired Multi-Scale Contourlet Attention Networks

Liu, M., Jiao, L., Liu, X., Li, L., Liu, F., Yang, S., & Zhang, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3304448 · Cited by 12

CORE: CORrelation-Guided Feature Enhancement for Few-Shot Image Classification

Xu, J., Pan, X., Wang, J., Pei, W., Liao, Q., & Xu, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3355774 · Cited by 10

Emerging Synergies in Causality and Deep Generative Models: A Survey

Zhou, G., Xie, S., Hao, G.-Y., Chen, S., Huang, B., Xu, X., Wang, C., Zhu, L., Yao, L., & Zhang, K. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3646958 · Cited by 29

Learning the Synthesizability of Dynamic Texture Samples

Yang, F., Xia, G.-S., Dai, D., & Zhang, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886807 · Cited by 10

Effective Characterization of Relief Patterns

Giachetti, A. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13493 · Cited by 10

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Turajlić, E., Begović, A., & Škaljo, N. (2019) · Electronics · doi:10.3390/electronics8020163 · Cited by 10

Gaussian Transfer Convolutional Neural Networks

Wang, H., Li, C., Zhen, X., Yang, W., & Zhang, B. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2881225 · Cited by 10

Few-shot hypercolumn-based mitochondria segmentation in cardiac and outer hair cells in focused ion beam-scanning electron microscopy (FIB-SEM) data

Dietlmeier, J., McGuinness, K., Rugonyi, S., Wilson, T., Nuttall, A. L., & O'Connor, N. E. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.10.031 · Cited by 11

Adversarial training for fast arbitrary style transfer

Xu, Z., Wilber, M. J., Chen, F., Hertzmann, A., & Jin, H. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.01.001 · Cited by 10

GAGCN: Generative adversarial graph convolutional network for non‐homogeneous texture extension synthesis

Xie, S., Qian, W., Nie, R., Xu, D., & Cao, J. (2023) · IET Image Processing · doi:10.1049/ipr2.12741 · Cited by 10

BoFF: A bag of fuzzy deep features for texture recognition

Florindo, J. B., & Esmi, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119627 · Cited by 12

Similarity contrastive estimation for image and video soft contrastive self-supervised learning

Denize, J., Rabarisoa, J., Orcesi, A., & Hérault, R. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01444-9 · Cited by 11

Incorporating rotational invariance in convolutional neural network architecture

Kandi, H., Jain, A., Chathoth, S. V., Mishra, D., & Subrahmanyam, G. R. K. S. (2018) · Pattern Analysis and Applications · doi:10.1007/s10044-018-0689-0 · Cited by 11

Co-occurrence based texture synthesis

Darzi, A., Lang, I., Taklikar, A., Averbuch‐Elor, H., & Avidan, S. (2021) · Computational Visual Media · doi:10.1007/s41095-021-0243-7 · Cited by 13

GANosaic: Mosaic Creation with Generative Texture ManifoldsPreprint

Jetchev, N., Bergmann, U., & Seward, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00269 · Cited by 10

Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?Preprint

Kortylewski, A., Egger, B., Morel-Forster, A., Schneider, A., Gerig, T., Blumer, C., Reyneke, C., & Vetter, T. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08565 · Cited by 10

Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image TransformationsPreprint

Yamaguchi, S., Kanai, S., Shioda, T., & Takeda, S. (2019) · arXiv (Cornell University) · Cited by 8

Deep Ensembles for Low-Data Transfer LearningPreprint

Mustafa, B., Riquelme, C., Puigcerver, J., Pinto, A. S., Keysers, D., & Houlsby, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06866 · Cited by 23

Visual feature representation in microscopy image classification

Song, Y., & Cai, W. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-814972-0.00004-7 · Cited by 9

Minimum description length clustering to measure meaningful image complexity

Mahon, L., & Lukasiewicz, T. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109889 · Cited by 9

Feature Adaptation with CLIP for Few-shot Classification

Wu, G., Chen, J., Zhang, W., & Wang, R. (2023) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3595916.3626396 · Cited by 9

Proto-Adapter: Efficient Training-Free CLIP-Adapter for Few-Shot Image Classification

Kato, N., Nota, Y., & Aoki, Y. (2024) · Sensors · doi:10.3390/s24113624 · Cited by 9

HybridPrompt: Domain-Aware Prompting for Cross-Domain Few-Shot Learning

Wu, J., Zhang, T., & Zhang, Y. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02086-8 · Cited by 11

Comprehensive Evaluation of ImageNet-Trained CNNs for Texture-Based Rock Classification

Mandal, D. J., Deborah, H., Tobing, T. L., Janiszewski, M., Tanaka, J. W., & Lawrance, A. K. (2024) · IEEE Access · doi:10.1109/access.2024.3424931 · Cited by 9

Selective Vision-Language Subspace Projection for Few-shot CLIP

Zhu, X., Zhu, B., Tan, Y., Wang, S., Hao, Y., & Zhang, H. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680885 · Cited by 10

Image inpainting for periodic discrete density defects via frequency analysis and an adaptive transformer-GAN network

Ding, H., Huang, Y., Chen, N., Lu, J., & Li, S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112410 · Cited by 9

Metric‐based pill recognition with the help of textual and visual cues

Rádli, R., Vörösházi, Z., & Czúni, L. (2024) · IET Image Processing · doi:10.1049/ipr2.13273 · Cited by 9

Unleash the Power of Vision-Language Models by Visual Attention Prompt and Multimodal Interaction

Zhang, W., Wu, L., Zhang, Z., Yu, T., Ma, C., Jin, X., Yang, X., & Zeng, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521785 · Cited by 14

Task-to-Instance Prompt Learning for Vision-Language Models at Test Time

Lu, Z., Bai, J., Li, X., Xiao, Z., & Wang, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3546840 · Cited by 11

MIF: Multi-source information fusion for few-shot classification with CLIP

Chen, J., Yuan, H., & Xie, B. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.03.025 · Cited by 9

Fully fine-tuned CLIP models are efficient few-shot learners

Liu, M., Li, B., Dan, J., Lu, Z., Wang, Z., & Yu, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113819 · Cited by 13

Knowledge Distillation-Based Anomaly Detection via Adaptive Discrepancy Optimization

Li, N., Liu, A., Zhu, Z., Lin, X., Ma, H., Dai, H., & Liang, Y. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3574540 · Cited by 11

tGARD: Text-Guided Adversarial Reconstruction for Industrial Anomaly Detection

Qiang, Y., Cao, J., Zhou, S., Yang, J., Yu, L., & Liu, B. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3598429 · Cited by 9

Recent Advances of Multimodal Continual Learning: A Comprehensive Survey

Yu, D., Zhang, X., Chen, Y., Liu, A., Zhang, Y., Yu, P. S., & King, I. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3658485 · Cited by 52

Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns

Sharma, G., & Jurie, F. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.09.007 · Cited by 9

Identifying the origin of Iris images based on fusion of local image descriptors and PRNU based techniques

Kauba, C., Debiasi, L., & Uhl, A. (2017) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272710 · Cited by 9

Simultaneous Material Segmentation and 3D Reconstruction in Industrial Scenarios

Zhao, C., Sun, L., & Stolkin, R. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00052 · Cited by 9

A completed parted region local neighborhood energy pattern for texture classification

Li, B., Li, Y., & Wu, Q. M. J. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104031 · Cited by 9

Dual-Branch Learning With Prior Information for Surface Anomaly Detection

Wang, S., Lv, C., Zhang, Z., & Xue-yan, W. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3300458 · Cited by 9

Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting

Huang, Q., Dong, X., Chen, D., Chen, Y., Yuan, L., Hua, G., Zhang, W., & Yu, N. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00154 · Cited by 12

Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models

Zheng, K., Wu, W., Feng, R., Zhu, K., Liu, J., Zhao, D., Zha, Z.-J., Chen, W., & Shen, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01071 · Cited by 11

A Novel Learnable Orthogonal Wavelet Unit Neural Network with Perfection Reconstruction Constraint Relaxation for Image Classification

Le, A. D., Jin, S., Bae, Y. S., & Nguyen, T. Q. (2023) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip59821.2023.10402772 · Cited by 9

YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection

Zolfi, A., Amit, G., Baras, A., Koda, S., Morikawa, I., Elovici, Y., & Shabtai, A. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00553 · Cited by 12

Part and Attribute Discovery from Relative Annotations

Maji, S., & Shakhnarovich, G. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0716-6 · Cited by 9

Computing fractal descriptors of texture images using sliding boxes: An application to the identification of Brazilian plant species

Taraschi, G., & Florindo, J. B. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123651 · Cited by 9

OODformer: Out-Of-Distribution Detection Transformer

Koner, R., Sinhamahapatra, P., Roscher, K., Günnemann, S., & Volker Tresp, C. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.382 · Cited by 10

Grayscale-inversion and rotation invariant image description using local ternary derivative pattern with dominant structure encoding

Song, T., Han, Y., Li, S., & Zhao, C. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116327 · Cited by 9

Deep convolutional neural networks for regular texture recognition

Liu, N., Rogers, M., Cui, H. L., Liu, W., Li, X., & Delmas, P. J. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.869 · Cited by 11

RONF: Reliable Outlier Synthesis under Noisy Feature Space for Out-of-Distribution Detection

He, R., Han, Z., Lu, X., & Yin, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547815 · Cited by 9

Evolutionary neural architecture search based on efficient CNN models population for image classification

Termritthikun, C., Jamtsho, Y., Muneesawang, P., Zhao, J. X., & Lee, I. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14187-y · Cited by 9

Adversarial Attacks and Batch Normalization: A Batch Statistics Perspective

Muhammad, A., Shamshad, F., & Bae, S. (2023) · IEEE Access · doi:10.1109/access.2023.3250661 · Cited by 10

ECO: Ensembling Context Optimization for Vision-Language Models

Agnolucci, L., Baldrati, A., Todino, F., Becattini, F., Bertini, M., & Bimbo, A. D. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00299 · Cited by 10

Measuring Self-Supervised Representation Quality for Downstream Classification Using Discriminative Features

Kalibhat, N., Narang, K., Firooz, H., Sanjabi, M., & Feizi, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29201 · Cited by 17

A visual analysis on recognizability and discriminability of onomatopoeia words with DCNN features

Shimoda, W., & Yanai‬, ‪. (2015) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2015.7177453 · Cited by 9

Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding

Ahmad, S., & Cheong, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_3 · Cited by 9

Texture Classification using Multiple Local Descriptors

Oraibi, Z. A., Irio, M., Hafiane, A., & Palaniappan, K. (2017) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2017.8457968 · Cited by 9

User-controllable Multi-texture Synthesis with Generative Adversarial Networks

Alanov, A., Kochurov, M., Volkhonskiy, D., Yashkov, D., Burnaev, E. V., & Vetrov, D. (2020) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0008924502140221 · Cited by 13

A short feature vector for image matching: The Log-Polar Magnitude feature descriptor

Matuszewski, D. J., Hast, A., Wählby, C., & Sintorn, I. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188496 · Cited by 9

One-shot Texture SegmentationPreprint

Ustyuzhaninov, I., Michaelis, C., Brendel, W., & Bethge, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.02654 · Cited by 12

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Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution FilterPreprint

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Concept Explainability for Plant Diseases Classification

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Synergistic Combination of Visual Features in Vision–Taste Crossmodal Correspondences

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Renyi entropy analysis of a deep convolutional representation for texture recognition

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VEDALDI A.: Describing textures in the wild

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Deep filter banks for texture recognition and segmentation

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Structured Textons for texture representation

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