Automated Flower Classification over a Large Number of Classes

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Histograms of Oriented Gradients for Human Detection

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Learning with Kernels

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images

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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

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Multiple kernel learning, conic duality, and the SMO algorithm

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Representing shape with a spatial pyramid kernel

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Towards fine-grained maize tassel flowering status recognition: Dataset, theory and practice

Lü, H., Cao, Z., Xiao, Y., Fang, Z., & Zhu, Y. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.02.026 · Cited by 20

Efficient and lightweight convolutional neural network architecture search methods for object classification

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Adversarial Reinforcement Learning Based Data Poisoning Attacks Defense for Task-Oriented Multi-User Semantic Communication

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Inference guided feature generation for generalized zero-shot learning

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Greedy auto-augmentation for n-shot learning using deep neural networks

Naghizadeh, A., Metaxas, D., & Liu, D. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.11.015 · Cited by 20

Integration of Leaky-Integrate-and-Fire Neurons in Standard Machine Learning Architectures to Generate Hybrid Networks: A Surrogate Gradient Approach

Gerum, R. C., & Schilling, A. (2021) · Neural Computation · doi:10.1162/neco_a_01424 · Cited by 21

Dynamic Perception Framework for Fine-Grained Recognition

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Discriminative deep attributes for generalized zero-shot learning

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Making a Bird AI Expert Work for You and Me

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Leveraging Balanced Semantic Embedding for Generative Zero-Shot Learning

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A three-stage GAN model based on edge and color prediction for image outpainting

Wei, G., Guo, J. M., Ke, Y., Wang, K., Yang, S., & Sheng, N. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119136 · Cited by 20

Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network

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GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation

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Generative Negative Text Replay for Continual Vision-Language Pretraining

Yan, S., Hong, L., Xu, H., Han, J., Tuytelaars, T., Li, Z., & He, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_2 · Cited by 21

Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net

Zhou, Y., Wong, L. N. Y., & Tse, K. K. C. (2023) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-023-03235-0 · Cited by 24

RoseTracker: A system for automated rose growth monitoring

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A Data-Based Perspective on Transfer Learning

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Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

Mou, C., Liang, A., Hu, C., Meng, F., Han, B., & Xu, F. (2023) · Animals · doi:10.3390/ani13203168 · Cited by 25

Fine-Grained Visual Prompt Learning of Vision-Language Models for Image Recognition

Sun, H., He, X., Zhou, J., & Peng, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612403 · Cited by 24

Prototype-Augmented Self-Supervised Generative Network for Generalized Zero-Shot Learning

Wu, J., Zhang, T., Zha, Z.-J., Luo, J.-B., Zhang, Y., & Wu, F. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3351439 · Cited by 19

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FrankenSplit: Efficient Neural Feature Compression With Shallow Variational Bottleneck Injection for Mobile Edge Computing

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Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

Das, S., Jain, T., Reilly, D., Balaji, P., Karmakar, S., Marjit, S., Li, X., Das, A., & Ryoo, M. S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00673 · Cited by 23

Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning

Jishnu Jaykumar, P., Palanisamy, K., Chao, Y.-W., Du, X., & Yu, X. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801660 · Cited by 27

Spectral error correcting output codes for efficient multiclass recognition

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Flower recognition system based on color and GIST features

Lodh, A., & Parekh, R. (2017) · Devices for Integrated Circuit (DevIC) · doi:10.1109/devic.2017.8074061 · Cited by 20

Semantically Modeling of Object and Context for Categorization

Zhang, C., Cheng, J., & Tian, Q. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2856096 · Cited by 20

Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics

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Real-Time Vehicle Make and Model Recognition Using Unsupervised Feature Learning

Nazemi, A., Azimifar, Z., Shafiee, M. J., et al. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2924830 · Cited by 20

Ancient Painting to Natural Image: A New Solution for Painting Processing

Qiao, T., Zhang, W., Zhang, M., Ma, Z., & Xu, D. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00061 · Cited by 22

Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

Huang, X., Wang, M., & Gong, M. (2019) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2019.00018 · Cited by 20

Effective Data Augmentation with Multi-Domain Learning GANs

Yamaguchi, S., Kanai, S., & Eda, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6131 · Cited by 21

Learning Multiple Parameters for Kernel Collaborative Representation Classification

Liu, J., Wu, Z., Xiao, L., & Yan, H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2962878 · Cited by 20

Learning efficient text-to-image synthesis via interstage cross-sample similarity distillation

Mao, F., Ma, B., Chang, H., Shan, S., & Chen, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2900-x · Cited by 21

DGattGAN: Cooperative Up-Sampling Based Dual Generator Attentional GAN on Text-to-Image Synthesis

Zhang, H., Zhu, H., Yang, S., & Li, W. (2021) · IEEE Access · doi:10.1109/access.2021.3058674 · Cited by 23

NeurIPS 2020 Competition: Predicting Generalization in Deep LearningPreprint

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Adversarial Prompt Tuning for Vision-Language Models

Zhang, J., Ma, X., Wang, X., Qiu, L., Wang, J., Jiang, Y., & Sang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72995-9_4 · Cited by 27

A review of few-shot fine-grained image classification

Lim, J. M., Lim, K. M., Lee, C. P., & Lim, J. Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127054 · Cited by 25

Logits DeConfusion with CLIP for Few-Shot Learning

Li, S., Liu, F., Hao, Z., Wang, X., Li, L., Liu, X., Chen, P., & Ma, W. (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/cvpr52734.2025.02366 · Cited by 26

A further study on biologically inspired feature enhancement in zero-shot learning

Xie, Z., Cao, W., & Ming, Z. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01170-y · Cited by 19

Augmented semantic feature based generative network for generalized zero-shot learning

Li, Z., Chen, Q., & Liu, Q. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.04.014 · Cited by 18

A Low Rank Promoting Prior for Unsupervised Contrastive Learning

Wang, Y., Lin, J., Cai, Q., Pan, Y., Yao, T., Chao, H., & Mei, T. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3180995 · Cited by 22

R-GAN: Exploring Human-like Way for Reasonable Text-to-Image Synthesis via Generative Adversarial Networks

Qiao, Y., Chen, Q., Deng, C., Ding, N., Qi, Y., Tan, M., Ren, X., & Wu, Q. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475363 · Cited by 19

GAN-MVAE: a discriminative latent feature generation framework for generalized zero-shot learning

Ma, P., Lu, H., Yang, B., & Ran, W. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.002 · Cited by 18

RepNet: Efficient On-Device Learning via Feature Reprogramming

Yang, L., Rakin, A. S., & Fan, D. (2022) · 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/cvpr52688.2022.01196 · Cited by 20

Meta Attention-Generation Network for Cross-Granularity Few-Shot Learning

Qiang, W., Li, J., Su, B., Fu, J., Xiong, H., & Wen, J.-R. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01760-7 · Cited by 19

Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning

Kim, S., Bae, S., & Yun, S.-Y. (2023) · 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/cvpr52729.2023.00728 · Cited by 20

Weighted side-window based gradient guided image filtering

Yuan, W., Cai, M., & Bai, X. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110006 · Cited by 19

Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?

Wu, C.-E. R., Tian, Y., Yu, H., Wang, H., Morgado, P., Hu, Y. H., & Yang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01420 · Cited by 24

Task-aware Adaptive Learning for Cross-domain Few-shot Learning

Guo, Y., Du, R., Dong, Y., Hospedales, T. M., Song, Y.-Z., & Ma, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00153 · Cited by 19

Adaptive Conditional Denoising Diffusion Model With Hybrid Affinity Regularizer for Generalized Zero-Shot Learning

Gao, M., & Dong, Q. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3359238 · Cited by 21

SA²VP: Spatially Aligned-and-Adapted Visual Prompt

Pei, W., Xia, T., Chen, F., Li, J., Tian, J., & Lu, G. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28243 · Cited by 21

CoPL: Contextual Prompt Learning for Vision-Language Understanding

Goswami, K., Karanam, S., Udhayanan, P., Joseph, K. J., & Srinivasan, B. V. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29766 · Cited by 25

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

Li, B., Xu, X., Wang, X., Hou, Y., Feng, Y., Wang, F., Zhang, X., Zhu, Q., & Che, W. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28084 · Cited by 21

CHAOS THEORY, ADVANCED METAHEURISTIC ALGORITHMS AND THEIR NEWFANGLED DEEP LEARNING ARCHITECTURE OPTIMIZATION APPLICATIONS: A REVIEW

Akgül, A., Karaca, Y., Pala, M. A., Çimen, M. E., Boz, A. F., & Yıldız, M. Z. (2024) · Fractals · doi:10.1142/s0218348x24300010 · Cited by 22

Domain Aligned CLIP for Few-shot Classification

Gondal, M. W., Gast, J., Ruiz, I. A., Droste, R., Macri, T., Kumar, S., & Staudigl, L. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00562 · Cited by 20

Multi-task prompt tuning with soft context sharing for vision–language models

Ding, K., Wang, Y., Liu, P., Yu, Q., Zhang, H., Xiang, S., & Pan, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128290 · Cited by 24

FSCNN: Fuzzy Channel Filter-Based Separable Convolution Neural Networks for Medical Imaging Recognition

Huang, H., Oh, S., Fu, Z., Wu, C.-K., Pedrycz, W., & Kim, J.-Y. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3450000 · Cited by 20

Pl@ntNet Mobile 2014

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Scalable Nonlinear Embeddings for Semantic Category-Based Image Retrieval

Sharma, G., & Schiele, B. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.153 · Cited by 19

On the Exploration of Convolutional Fusion Networks for Visual Recognition

Liu, Y., Guo, Y., & Lew, M. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51811-4_23 · Cited by 19

Model Selection for Generalized Zero-Shot Learning

Zhang, H., & Koniusz, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11012-3_16 · Cited by 20

Deep embedding learning with adaptive large margin N-pair loss for image retrieval and clustering

Chen, B., & Deng, W. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.05.011 · Cited by 19

Towards Making Deep Transfer Learning Never Hurt

Wan, R., Xiong, H., Li, X., Zhu, Z., & Huan, J. (2019) · Proceedings · doi:10.1109/icdm.2019.00068 · Cited by 19

Improving Generative Adversarial Networks With Local Coordinate Coding

Cao, J., Guo, Y. F., Wu, Q., Shen, C., Huang, J., & Tan, M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3012096 · Cited by 47

S2IGAN: Speech-to-Image Generation via Adversarial Learning

Wang, X., Qiao, T., Zhu, J., Hanjalić, A., & Scharenborg, O. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1759 · Cited by 19

SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs

Hartley, T., Sidorov, K. A., Willis, C., & Marshall, D. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00047 · Cited by 19

DCFGAN: Dynamic Convolutional Fusion Generative Adversarial Network for Text-to-Image Synthesis

Tao, M., Wu, S., Zhang, X., & Wang, C. (2020) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba50161.2020.9277299 · Cited by 19

An efficient deep learning model for cultivar identification of a pistachio tree

Heidary-Sharifabad, A., Zarchi, M. S., Emadi, S., & Zarei, G. (2021) · British Food Journal · doi:10.1108/bfj-12-2020-1100 · Cited by 18

Robust and Attack Resilient Logic Locking with a High Application-Level Impact

Liu, Y., Zuzak, M., Xie, Y., Chakraborty, A., & Srivastava, A. (2021) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3446215 · Cited by 20

Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models

Kim, B. C., Kim, H. C., Han, S.-H., & Park, D. K. (2022) · Sensors · doi:10.3390/s22124392 · Cited by 19

ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

Wang, J., Lu, G., Xu, H., Li, Z., Xu, C., & Fu, Y. (2022) · 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/cvpr52688.2022.01044 · Cited by 21

Efficient Optimization for Low-Rank Integrated Bilinear Classifiers

Kobayashi, T., & Otsu, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_34 · Cited by 19

Analytical review on object segmentation and recognition

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Continual Learning in Neural NetworksPreprint

Aljundi, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02718 · Cited by 34

SegAttnGAN: Text to Image Generation with Segmentation AttentionPreprint

Gou, Y., Wu, Q., Li, M., Gong, B., & Han, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.12444 · Cited by 19

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification BenchmarkPreprint

Dumoulin, V., Houlsby, N., Evci, U., Zhai, X., Goroshin, R., Gelly, S., & Larochelle, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02638 · Cited by 42

Speedy Performance Estimation for Neural Architecture SearchPreprint

Ru, B., Lyle, C., Schut, L., Fil, M., van der Wilk, M., & Gal, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04492 · Cited by 19

Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining ParadigmsPreprint

Yang, X., He, X., Liang, Y., Yang, Y., Zhang, S., & Xie, P. (2020) · doi:10.36227/techrxiv.12502298 · Cited by 22

Are Deep Neural Architectures Losing Information? Invertibility is Indispensable

Liu, Y., Qin, Z., Anwar, S., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63836-8_15 · Cited by 18

Zero-shot learning with self-supervision by shuffling semantic embeddings

Kim, H., Lee, J., & Byun, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.037 · Cited by 16

Learning Aligned Image-Text Representations Using Graph Attentive Relational Network

Jing, Y., Wang, W., Wang, L., & Tan, T. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3048627 · Cited by 19

Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset

Litvak, M., Divekar, S., & Rabaev, I. (2022) · Signals · doi:10.3390/signals3030031 · Cited by 18

DualG-GAN, a Dual-channel Generator based Generative Adversarial Network for text-to-face synthesis

Luo, X., He, X., Chen, X., Qing, L., & Zhang, J. C. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.08.016 · Cited by 18

A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint

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Manifold Learning Benefits GANs

Ni, Y., Koniusz, P., Hartley, R. I., & Nock, R. (2022) · 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/cvpr52688.2022.01098 · Cited by 22

Learning Visual Representations via Language-Guided Sampling

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Coping with change: Learning invariant and minimum sufficient representations for fine-grained visual categorization

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Data-Free Generalized Zero-Shot Learning

Tang, B., Zhang, J., Yan, L., Yu, Q., Sheng, L., & Xu, D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28316 · Cited by 19

An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing

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MHAFF: Multihead Attention Feature Fusion of CNN and Transformer for Cattle Identification

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On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions

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Multimodal Federated Learning: A Survey

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