{"total":195,"rows":[{"id":"2103.14899","title":"CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification","created":"2021-03-27","cited_by_count":806,"area":null},{"id":"2111.11432","title":"Florence: A New Foundation Model for Computer Vision","created":"2021-11-22","cited_by_count":804,"area":null},{"id":"2207.10666","title":"TinyViT: Fast Pretraining Distillation for Small Vision Transformers","created":"2022-07-21","cited_by_count":234,"area":null},{"id":"2205.06743","title":"A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities","created":"2022-05-13","cited_by_count":163,"area":null},{"id":"2103.13517","title":"A Broad Study on the Transferability of Visual Representations with Contrastive Learning","created":"2021-03-24","cited_by_count":78,"area":null},{"id":"2302.09309","title":"StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning","created":"2023-02-18","cited_by_count":53,"area":null},{"id":"2106.07807","title":"Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data","created":"2021-06-14","cited_by_count":50,"area":null},{"id":"2111.09613","title":"Improving Transferability of Representations via Augmentation-Aware Self-Supervision","created":"2021-11-18","cited_by_count":49,"area":null},{"id":"2107.11978","title":"Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data","created":"2021-07-26","cited_by_count":48,"area":null},{"id":"2202.01339","title":"Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty","created":"2022-02-01","cited_by_count":37,"area":null},{"id":"2112.00496","title":"Revisiting the Transferability of Supervised Pretraining: an MLP Perspective","created":"2021-12-01","cited_by_count":36,"area":null},{"id":"2306.04557","title":"PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain","created":"2023-06-07","cited_by_count":31,"area":null},{"id":"2112.11450","title":"Max-Margin Contrastive Learning","created":"2021-12-21","cited_by_count":27,"area":null},{"id":"2403.00567","title":"Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning","created":"2024-03-01","cited_by_count":26,"area":null},{"id":"2203.07656","title":"Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning","created":"2022-03-15","cited_by_count":23,"area":null},{"id":"2303.00996","title":"Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning","created":"2023-03-02","cited_by_count":15,"area":null},{"id":"2306.10511","title":"Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning","created":"2023-06-18","cited_by_count":15,"area":null},{"id":"2112.08181","title":"Hierarchical Variational Memory for Few-shot Learning Across Domains","created":"2021-12-15","cited_by_count":14,"area":null},{"id":"2303.08557","title":"Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey","created":"2023-03-15","cited_by_count":14,"area":null},{"id":"2206.01862","title":"Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16","created":"2022-06-04","cited_by_count":13,"area":null},{"id":"2209.02377","title":"MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves","created":"2022-08-27","cited_by_count":13,"area":null},{"id":"2309.03989","title":"CDFSL-V: Cross-Domain Few-Shot Learning for Videos","created":"2023-09-07","cited_by_count":13,"area":null},{"id":"2403.01966","title":"Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning","created":"2024-03-04","cited_by_count":13,"area":null},{"id":"2310.15105","title":"FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning","created":"2023-10-23","cited_by_count":12,"area":null},{"id":"2101.02190","title":"A Robust Illumination-Invariant Camera System for Agricultural Applications","created":"2021-01-06","cited_by_count":11,"area":null},{"id":"2101.07899","title":"Cross-domain few-shot learning with unlabelled data","created":"2021-01-19","cited_by_count":10,"area":null},{"id":"2412.00767","title":"Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting","created":"2024-12-01","cited_by_count":10,"area":null},{"id":"2103.09408","title":"WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting","created":"2021-03-17","cited_by_count":8,"area":null},{"id":"2207.04398","title":"Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation","created":"2022-07-10","cited_by_count":8,"area":null},{"id":"2208.06270","title":"RenyiCL: Contrastive Representation Learning with Skew Renyi Divergence","created":"2022-08-12","cited_by_count":8,"area":null},{"id":"2302.12712","title":"Amortised Invariance Learning for Contrastive Self-Supervision","created":"2023-02-24","cited_by_count":8,"area":null},{"id":"2106.07770","title":"Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection","created":"2021-06-14","cited_by_count":7,"area":null},{"id":"2208.02707","title":"Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2209.08622","title":"The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning","created":"2022-09-18","cited_by_count":7,"area":null},{"id":"2304.09876","title":"Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing","created":"2023-04-19","cited_by_count":7,"area":null},{"id":"2401.13987","title":"Cross-Domain Few-Shot Learning via Adaptive Transformer Networks","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2510.09201","title":"Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs","created":"2025-10-10","cited_by_count":7,"area":null},{"id":"2102.04515","title":"Leaf Image-based Plant Disease Identification using Color and Texture Features","created":"2021-02-08","cited_by_count":6,"area":null},{"id":"2112.09190","title":"Monitoring crop phenology with street-level imagery using computer vision","created":"2021-12-16","cited_by_count":6,"area":null},{"id":"2205.05282","title":"ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning","created":"2022-05-11","cited_by_count":6,"area":null},{"id":"2205.07874","title":"How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation","created":"2022-05-13","cited_by_count":6,"area":null},{"id":"2304.04858","title":"Simulated Annealing in Early Layers Leads to Better Generalization","created":"2023-04-10","cited_by_count":6,"area":null},{"id":"2402.02444","title":"BECLR: Batch Enhanced Contrastive Few-Shot Learning","created":"2024-02-04","cited_by_count":6,"area":null},{"id":"2504.21706","title":"Vision Transformers in Precision Agriculture: A Comprehensive Survey","created":"2025-04-30","cited_by_count":6,"area":null},{"id":"2205.00895","title":"On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification","created":"2022-05-02","cited_by_count":5,"area":null},{"id":"2207.03881","title":"The Power of Transfer Learning in Agricultural Applications: AgriNet","created":"2022-07-08","cited_by_count":5,"area":null},{"id":"2207.07919","title":"Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT","created":"2022-07-16","cited_by_count":5,"area":null},{"id":"2301.01200","title":"Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications","created":"2022-12-20","cited_by_count":5,"area":null},{"id":"2305.04994","title":"Crop identification using deep learning on LUCAS crop cover photos","created":"2023-05-08","cited_by_count":5,"area":null},{"id":"2404.19644","title":"MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation","created":"2024-04-30","cited_by_count":5,"area":null},{"id":"2408.09722","title":"Towards Few-Shot Learning in the Open World: A Review and Beyond","created":"2024-08-19","cited_by_count":5,"area":null},{"id":"2603.05235","title":"Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning","created":"2026-03-05","cited_by_count":5,"area":null},{"id":"2106.03041","title":"DAMSL: Domain Agnostic Meta Score-based Learning","created":"2021-06-06","cited_by_count":4,"area":null},{"id":"2109.01302","title":"Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition","created":"2021-09-03","cited_by_count":4,"area":null},{"id":"2202.09014","title":"How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?","created":"2022-02-18","cited_by_count":4,"area":null},{"id":"2206.07125","title":"Self-Supervised Pretraining for Differentially Private Learning","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2206.11623","title":"Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering","created":"2022-06-23","cited_by_count":4,"area":null},{"id":"2208.08015","title":"Cross-Domain Few-Shot Classification via Inter-Source Stylization","created":"2022-08-17","cited_by_count":4,"area":null},{"id":"2210.06339","title":"Self-Attention Message Passing for Contrastive Few-Shot Learning","created":"2022-10-12","cited_by_count":4,"area":null},{"id":"2304.06520","title":"An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification","created":"2023-04-10","cited_by_count":4,"area":null},{"id":"2305.11533","title":"Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2308.06668","title":"Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges","created":"2023-08-13","cited_by_count":4,"area":null},{"id":"2309.01342","title":"Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification","created":"2023-09-04","cited_by_count":4,"area":null},{"id":"2409.07989","title":"Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms","created":"2024-09-12","cited_by_count":4,"area":null},{"id":"2503.16628","title":"MobilePlantViT: A Mobile-friendly Hybrid ViT for Generalized Plant Disease Image Classification","created":"2025-03-20","cited_by_count":4,"area":null},{"id":"2506.02843","title":"Random Registers for Cross-Domain Few-Shot Learning","created":"2025-06-03","cited_by_count":4,"area":null},{"id":"2507.11279","title":"Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping","created":"2025-07-15","cited_by_count":4,"area":null},{"id":"2508.14486","title":"WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classification","created":"2025-08-20","cited_by_count":4,"area":null},{"id":"2605.06714","title":"Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey","created":"2026-05-07","cited_by_count":4,"area":null},{"id":"2104.00619","title":"Modular Adaptation for Cross-Domain Few-Shot Learning","created":"2021-04-01","cited_by_count":3,"area":null},{"id":"2104.03309","title":"Streaming Self-Training via Domain-Agnostic Unlabeled Images","created":"2021-04-07","cited_by_count":3,"area":null},{"id":"2111.12265","title":"Distribution Estimation to Automate Transformation Policies for Self-Supervision","created":"2021-11-24","cited_by_count":3,"area":null},{"id":"2205.11518","title":"LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation","created":"2022-05-23","cited_by_count":3,"area":null},{"id":"2206.04374","title":"Uncovering bias in the PlantVillage dataset","created":"2022-06-09","cited_by_count":3,"area":null},{"id":"2303.09253","title":"A Survey of Deep Visual Cross-Domain Few-Shot Learning","created":"2023-03-16","cited_by_count":3,"area":null},{"id":"2311.02392","title":"Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification","created":"2023-11-04","cited_by_count":3,"area":null},{"id":"2503.20068","title":"iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2504.20419","title":"Plant Disease Detection through Multimodal Large Language Models and Convolutional Neural Networks","created":"2025-04-29","cited_by_count":3,"area":null},{"id":"2506.03110","title":"Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning","created":"2025-06-03","cited_by_count":3,"area":null},{"id":"2202.08149","title":"Self-Supervised Class-Cognizant Few-Shot Classification","created":"2022-02-15","cited_by_count":2,"area":null},{"id":"2209.12557","title":"Device-friendly Guava fruit and leaf disease detection using deep learning","created":"2022-09-26","cited_by_count":2,"area":null},{"id":"2306.06082","title":"Augmentation-aware Self-supervised Learning with Conditioned Projector","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2308.14267","title":"Unleash Model Potential: Bootstrapped Meta Self-supervised Learning","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2311.02338","title":"Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach","created":"2023-11-04","cited_by_count":2,"area":null},{"id":"2312.09589","title":"Improving Cross-domain Few-shot Classification with Multilayer Perceptron","created":"2023-12-15","cited_by_count":2,"area":null},{"id":"2402.17424","title":"ViTaL: An Advanced Framework for Automated Plant Disease Identification in Leaf Images Using Vision Transformers and Linear Projection For Feature Reduction","created":"2024-02-27","cited_by_count":2,"area":null},{"id":"2403.15248","title":"Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks","created":"2024-03-22","cited_by_count":2,"area":null},{"id":"2406.04772","title":"REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learning","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2507.04412","title":"SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights","created":"2025-07-06","cited_by_count":2,"area":null},{"id":"2507.15243","title":"Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation","created":"2025-07-21","cited_by_count":2,"area":null},{"id":"2102.03082","title":"Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders","created":"2021-02-05","cited_by_count":1,"area":null},{"id":"2103.11241","title":"Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop","created":"2021-03-20","cited_by_count":1,"area":null},{"id":"2103.15578","title":"Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks","created":"2021-03-29","cited_by_count":1,"area":null},{"id":"2107.04895","title":"Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning","created":"2021-07-10","cited_by_count":1,"area":null},{"id":"2204.11340","title":"Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions","created":"2022-04-24","cited_by_count":1,"area":null},{"id":"2301.07927","title":"Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning","created":"2023-01-19","cited_by_count":1,"area":null},{"id":"2302.08818","title":"Apple scab detection in orchards using deep learning on colour and multispectral images","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2308.00727","title":"Adaptive Semantic Consistency for Cross-domain Few-shot Classification","created":"2023-08-01","cited_by_count":1,"area":null},{"id":"2309.01903","title":"Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2404.16833","title":"Leaf-Based Plant Disease Detection and Explainable AI","created":"2023-12-17","cited_by_count":1,"area":null}],"next_cursor":"100"}