{"total":36,"rows":[{"id":"2207.11169","title":"Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark","created":"2022-07-22","cited_by_count":17,"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":"2212.14441","title":"Fruit Ripeness Classification: a Survey","created":"2022-12-29","cited_by_count":7,"area":null},{"id":"2607.06560","title":"Vision as Unified Multimodal Generation","created":"2026-07-07","cited_by_count":7,"area":null},{"id":"2210.11318","title":"A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture","created":"2022-10-20","cited_by_count":6,"area":null},{"id":"2303.18101","title":"INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields","created":"2023-03-31","cited_by_count":6,"area":null},{"id":"2202.11372","title":"Localizing Small Apples in Complex Apple Orchard Environments","created":"2022-02-23","cited_by_count":5,"area":null},{"id":"2309.05645","title":"CitDet: A Benchmark Dataset for Citrus Fruit Detection","created":"2023-09-11","cited_by_count":5,"area":null},{"id":"2411.16196","title":"Learn from Foundation Model: Fruit Detection Model without Manual Annotation","created":"2024-11-25","cited_by_count":5,"area":null},{"id":"2105.07660","title":"Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods","created":"2021-05-17","cited_by_count":4,"area":null},{"id":"2303.01598","title":"A Meta-Learning Approach to Predicting Performance and Data Requirements","created":"2023-03-02","cited_by_count":4,"area":null},{"id":"2407.04711","title":"MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models","created":"2024-05-14","cited_by_count":4,"area":null},{"id":"2311.05029","title":"S$^3$AD: Semi-supervised Small Apple Detection in Orchard Environments","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2407.13304","title":"A Dataset and Benchmark for Shape Completion of Fruits for Agricultural Robotics","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2202.08670","title":"Domain Randomization for Object Counting","created":"2022-02-17","cited_by_count":2,"area":null},{"id":"2306.14361","title":"A differentiable Gaussian Prototype Layer for explainable Segmentation","created":"2023-06-25","cited_by_count":2,"area":null},{"id":"2308.08271","title":"Detecting Olives with Synthetic or Real Data? Olive the Above","created":"2023-08-16","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":"2603.21229","title":"Plant Taxonomy Meets Plant Counting: A Fine-Grained, Taxonomic Dataset for Counting Hundreds of Plant Species","created":"2026-03-22","cited_by_count":2,"area":null},{"id":"2211.17170","title":"How to Train an Accurate and Efficient Object Detection Model on Any Dataset","created":"2022-11-30","cited_by_count":1,"area":null},{"id":"2402.14591","title":"High-Speed Detector For Low-Powered Devices In Aerial Grasping","created":"2024-02-22","cited_by_count":1,"area":null},{"id":"2405.02556","title":"Few-Shot Fruit Segmentation via Transfer Learning","created":"2024-05-04","cited_by_count":1,"area":null},{"id":"2508.18694","title":"AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2601.22198","title":"Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2603.27519","title":"SPROUT: A Scalable Diffusion Foundation Model for Multi-Crop Plant Phenotyping","created":"2026-03-29","cited_by_count":1,"area":null},{"id":"2605.22034","title":"AgroVG: A Large-Scale Multi-Source Benchmark for Agricultural Visual Grounding","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2104.13763","title":"LGA-RCNN: Loss-Guided Attention for Object Detection","created":"2021-04-28","cited_by_count":0,"area":null},{"id":"2209.05654","title":"ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers","created":"2022-09-13","cited_by_count":0,"area":null},{"id":"2409.01573","title":"Improving Apple Object Detection with Occlusion-Enhanced Distillation","created":"2024-09-03","cited_by_count":0,"area":null},{"id":"2411.00967","title":"Phenology-based learning framework for yield estimation and harvest forecasting of raspberry fruits","created":"2024-11-01","cited_by_count":0,"area":null},{"id":"2412.19439","title":"Paleoinspired Vision: From Exploring Colour Vision Evolution to Inspiring Camera Design","created":"2024-12-27","cited_by_count":0,"area":null},{"id":"2503.13188","title":"3D Hierarchical Panoptic Segmentation in Real Orchard Environments Across Different Sensors","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2505.14029","title":"AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2512.11884","title":"Generalization vs. Specialization: Evaluating Segment Anything Model (SAM3) Zero-Shot Segmentation Against Fine-Tuned YOLO Detectors","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2604.09996","title":"A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery","created":"2026-04-11","cited_by_count":0,"area":null},{"id":"2607.14934","title":"Still image and spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels","created":"2026-07-16","cited_by_count":0,"area":null}],"next_cursor":null}