Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development | Paper Citation Record

Observation 6e1f563d-2879-4637-a91e-9f481aa43af9 · outbound

This paper cites A survey on instance segmentation: state of the art,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A survey on instance segmentation: state of the art,

Reference 1

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This paper cites Utilizing deep learning in medical image analysis for en- hanced diagnostic accuracy and patient care: Challenges, opportunities, and ethical implications,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Utilizing deep learning in medical image analysis for en- hanced diagnostic accuracy and patient care: Challenges, opportunities, and ethical implications,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Machine learning empowering personalized medicine: A comprehensive review of med- ical image analysis methods,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Automatic tooth instance segmentation and identification from panoramic x-ray images using deep cnn,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Idd-net: A deep learning approach for early detection of dental diseases using x-ray imaging,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A traffic surveillance system for obtaining comprehensive information of the passing vehicles based on instance segmentation,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A virtual- real interaction approach to object instance segmentation in traffic scenes,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A review of mo- tion planning techniques for automated vehicles,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Perception, positioning and decision-making algorithms adaptation for an autonomous valet parking system based on infrastructure reference points using one single lidar,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Rtlseg: A novel multi-component inspection network for railway track line based on instance segmentation,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Bars: a benchmark for airport runway segmentation,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Automatic segmentation of airport pavement damage by am-mask r-cnn algorithm,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Using image analytics to monitor retail store shelves,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A comprehensive survey on computer vision based approaches for automatic identification of products in retail store,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Digital transformation of grocery in-store shopping-scanners, artificial intelligence, augmented reality and beyond: A review,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A yolo algorithm-based visitor detection system for small retail stores using single board computer,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Instance segmentation algorithm for sorting dismantling components of end- 18 of-life vehicles,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A novel mr remote collaborative assembly system using reconstructed attribute- enhanced product models,

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Dsn-br-based online inspection method and application for surface defects of pharmaceutical products in aluminum-plastic blister packages,

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Reference 32

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A review of robotic assem- bly strategies for the full operation procedure: planning, execution and evaluation,

Reference 33

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Reference 36

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Reference 37

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Reference 38

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Reference 40

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Reference 41

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Reference 42

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Reference 44

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Reference 45

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Cucumber fruits detection in greenhouses based on instance segmentation,

Reference 46

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Instance segmentation of root crops and simulation-based learning to estimate their physical dimensions for on-line machine vision yield monitoring,

Reference 47

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Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A fast and accurate deep learning method for strawberry instance segmentation,

Reference 48

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Observation 4173635c-880b-4247-bd00-76a5f00f04e8 · outbound

This paper cites Instance segmentation method for weed detection using uav imagery in soybean fields,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Instance segmentation method for weed detection using uav imagery in soybean fields,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:19.046096Z

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Observation 46edaeff-9ce5-4365-a6bd-2b053a715058 · outbound

This paper cites Dealing with clouds and seasonal changes for center pivot irrigation systems detection using instance segmentation in sentinel-2 time series,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Dealing with clouds and seasonal changes for center pivot irrigation systems detection using instance segmentation in sentinel-2 time series,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:19.034742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7db84dea-3a9c-45e2-9612-1c9b383cb899 · outbound

This paper cites Foveamask: A fast and accurate deep learning model for green fruit instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Foveamask: A fast and accurate deep learning model for green fruit instance segmentation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:19.020603Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb956409-eb21-4ba4-9a11-8bf86d220cb0 · outbound

This paper cites Deep learning-based instance seg- mentation architectures in agriculture: A review of the scopes and challenges,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Deep learning-based instance seg- mentation architectures in agriculture: A review of the scopes and challenges,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:19.005372Z

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Observation d08d01b8-7935-48be-8950-f1ca4c051e64 · outbound

This paper cites Fgn: Fully guided network for few-shot instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Fgn: Fully guided network for few-shot instance segmentation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.991037Z

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Observation a20b45b6-0a61-4dbf-a0d9-21a28239469f · outbound

This paper cites Incremental few-shot instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Incremental few-shot instance segmentation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.975720Z

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Observation aa55a17f-3ab1-4906-95c4-9cbf73546591 · outbound

This paper cites Reference twice: A simple and unified baseline for few- shot instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Reference twice: A simple and unified baseline for few- shot instance segmentation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.961745Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1fcfb599-4c64-4183-945b-f7709a0c747b · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Generalizing from a few examples: A survey on few-shot learning,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.703117Z

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Observation 309aa3f4-3dc7-47fa-be77-c85f019234f6 · outbound

This paper cites True few-shot learning with language models,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development True few-shot learning with language models,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.936761Z

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Observation 69367412-9a99-4a28-897b-855ce5eb89e6 · outbound

This paper cites Research progress on few-shot learning for remote sensing image interpretation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Research progress on few-shot learning for remote sensing image interpretation,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.713047Z

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Observation aa68e820-4276-4858-b0a6-587bb1e3dfc5 · outbound

This paper cites Celltranspose: Few-shot domain adaptation for cellular instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Celltranspose: Few-shot domain adaptation for cellular instance segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.913291Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5efd7a59-0386-42d1-8751-518fc0a6975f · outbound

This paper cites Dynamic transformer for few-shot instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Dynamic transformer for few-shot instance segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.899671Z

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Observation e775fecc-ab22-45ea-b6db-baf2758c7ca1 · outbound

This paper cites ifs-rcnn: An incremental few-shot instance segmenter,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development ifs-rcnn: An incremental few-shot instance segmenter,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.885673Z

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Observation 2c87c451-89fa-4cc3-ab02-30ea2d352947 · outbound

This paper cites Transfer and zero-shot learning for scalable weed detection and classification in uav images,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Transfer and zero-shot learning for scalable weed detection and classification in uav images,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.874195Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfabb8e7-2b53-45c8-a48b-6e0309d7b7f0 · outbound

This paper cites Alignzeg: Mitigating objective misalignment for zero-shot semantic segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Alignzeg: Mitigating objective misalignment for zero-shot semantic segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.862005Z

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Observation 2696c31c-dee2-41ba-bf6c-cb236acbe467 · outbound

This paper cites Generalized zero-shot learning for classifying unseen wafer map patterns,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Generalized zero-shot learning for classifying unseen wafer map patterns,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.850573Z

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Observation f7dc7e20-5d05-4764-a763-772602767cdc · outbound

This paper cites Zero-shot instance seg- mentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot instance seg- mentation,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.838825Z

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Observation 92f067b0-d2b8-465b-bd3f-4c95753fea39 · outbound

This paper cites Zero-shot unsupervised transfer instance segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot unsupervised transfer instance segmentation,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.825266Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57a2cfdf-8817-4db7-a72b-6af65d41f2e5 · outbound

This paper cites Zero-shot semantic segmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot semantic segmentation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.812608Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8879e6ab-5f33-4448-b1a8-7d44fe037adc · outbound

This paper cites Visual se- mantic segmentation based on few/zero-shot learning: An overview,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Visual se- mantic segmentation based on few/zero-shot learning: An overview,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.797786Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0bbf81ec-d0aa-4647-b05d-ee63da978397 · outbound

This paper cites Synthetic meets authentic: Leveraging llm generated datasets for yolo11 and yolov10-based apple detection through machine vision sensors,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Synthetic meets authentic: Leveraging llm generated datasets for yolo11 and yolov10-based apple detection through machine vision sensors,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.782038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae3fa9ae-7733-487a-bb0b-237e33355559 · outbound

This paper cites Text-to-image generation for abstract concepts,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Text-to-image generation for abstract concepts,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.765868Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3bcd314-c17b-4fa6-a5f0-90b85b8b4856 · outbound

This paper cites Twigma: A dataset of ai-generated images with metadata from twitter,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Twigma: A dataset of ai-generated images with metadata from twitter,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.750282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 417e74d5-7532-4ca8-8229-b303fbd74816 · outbound

This paper cites Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.735234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ceaf955-bfd1-46c4-881b-c5416bcb6b46 · outbound

This paper cites Ai-based image generator web application using openai’s dall-e sys- tem,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Ai-based image generator web application using openai’s dall-e sys- tem,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.721736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cc1604fb-1e2f-42c4-a9ee-ee02b55310a7 · outbound

This paper cites Segment anything,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Segment anything,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.709350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19ce4260-f5ae-44ce-a8fc-0387bf8ba3d4 · outbound

This paper cites Zero-shot object detection,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot object detection,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.696722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fbffd842-05bd-4b9d-94d4-f1f7293aef39 · outbound

This paper cites Zero shot detection,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero shot detection,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.682052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6f5fd303-a698-48cd-928e-6b0a48a8167a · outbound

This paper cites A review of generalized zero-shot learning meth- ods,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A review of generalized zero-shot learning meth- ods,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.816794Z

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Observation 86b215b1-510d-401d-8810-a5b2d0333b97 · outbound

This paper cites Zero-shot causal learning,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot causal learning,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.659361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 94528fef-c079-4d0d-85ca-88a56de8c255 · outbound

This paper cites Zero-shot learning by harnessing adversarial samples,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot learning by harnessing adversarial samples,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.645164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c6ce032-08c7-4ce0-8e3e-aa97dffde0bb · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient segment anything,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Efficientsam: Leveraged masked image pretraining for efficient segment anything,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.630825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a482618f-1ff1-44ed-98e6-55a616f1cd81 · outbound

This paper cites Segment anything model for med- ical image segmentation: Current applications and future directions,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Segment anything model for med- ical image segmentation: Current applications and future directions,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.838582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b249649-1b7c-4ece-8846-dbe096b6e388 · outbound

This paper cites The segment anything model (sam) for remote sensing applications: From zero to one shot,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development The segment anything model (sam) for remote sensing applications: From zero to one shot,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.843247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdfd1556-31ae-41d2-aab4-ff72ac9b2e63 · outbound

This paper cites Zero-shot segmentation of eye features using the segment anything model (sam),.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Zero-shot segmentation of eye features using the segment anything model (sam),

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.597830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e3b28492-849a-4ba3-b169-3eae897bdda3 · outbound

This paper cites An efficient segment anything model for the segmentation of medical images,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development An efficient segment anything model for the segmentation of medical images,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.584296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation edaf2695-28af-4a67-b217-36340dec7f41 · outbound

This paper cites YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development YOLO11 and Vision Transformers based 3D Pose Estimation of Immature Green Fruits in Commercial Apple Orchards for Robotic Thinning

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.859639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14631559-5781-4b0e-b9a3-648533a633ca · outbound

This paper cites Comprehensive performance evaluation of yolo11, yolov10, yolov9 and yolov8 on detecting and counting fruitlet in complex orchard environments,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Comprehensive performance evaluation of yolo11, yolov10, yolov9 and yolov8 on detecting and counting fruitlet in complex orchard environments,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.865188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1d0fb45-93c2-4bb3-a5c2-a0e9b94dbae6 · outbound

This paper cites Improving deep learning with generic data augmentation,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Improving deep learning with generic data augmentation,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.571383Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c950c63a-be57-40bd-a805-0239e3a30705 · outbound

This paper cites A survey on image data augmen- tation for deep learning,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A survey on image data augmen- tation for deep learning,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.876174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 213961d0-1e9a-4515-9450-c2c120713c3a · outbound

This paper cites Data augmentation: A comprehensive survey of modern approaches,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Data augmentation: A comprehensive survey of modern approaches,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.883226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b4c68e-7b5e-4353-9fe7-a68de9b18d20 · outbound

This paper cites Multi-modal llms in agriculture: A comprehensive review,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Multi-modal llms in agriculture: A comprehensive review,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.541917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 889f486e-0ca7-4133-bcbd-5e1924730df6 · outbound

This paper cites Transforma- tive technologies in digital agriculture: Leveraging internet of things, remote sensing, and artificial intelligence for smart crop management,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Transforma- tive technologies in digital agriculture: Leveraging internet of things, remote sensing, and artificial intelligence for smart crop management,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.526292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b9a1a85-afa5-421c-9e79-c0aeb419334e · outbound

This paper cites Mapping smart farming: Addressing agricultural challenges in data- driven era,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Mapping smart farming: Addressing agricultural challenges in data- driven era,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.511209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 745d4ffb-1610-49fa-81e1-f664b55ac493 · outbound

This paper cites A farmer- centric agricultural decision support system for market dynamics in a volatile agricultural supply chain,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development A farmer- centric agricultural decision support system for market dynamics in a volatile agricultural supply chain,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.483225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 988254ab-0f59-45ea-bcfa-3adef32176fe · outbound

This paper cites Climate-adaptive pest management for sustainable agriculture: Navigating temperature, precipitation, and evolving pest dynamics,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Climate-adaptive pest management for sustainable agriculture: Navigating temperature, precipitation, and evolving pest dynamics,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.468878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6c95f3b-c96c-4ea8-bf31-9f4235b7660e · outbound

This paper cites The impact of climate change on insect pest biology and ecology: Implications for pest management strategies, crop production, and food security,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development The impact of climate change on insect pest biology and ecology: Implications for pest management strategies, crop production, and food security,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.455708Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01c3dc83-e4b9-46b8-9f0a-62d9004e9c69 · outbound

This paper cites Immature green apple detection and sizing in commercial orchards using yolov8 and shape fitting techniques,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Immature green apple detection and sizing in commercial orchards using yolov8 and shape fitting techniques,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.919498Z

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Unavailable: canonical work link unavailable.

Observation 2a97062f-562f-4fdb-80e1-4aeaa9149341 · outbound

This paper cites Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:48:18.212598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2274fce2-72c6-44c8-b1e5-5bee21355c8c · outbound

This paper cites Yolov10 to its genesis: A decadal and comprehensive review of the you only look once series,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Yolov10 to its genesis: A decadal and comprehensive review of the you only look once series,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.429587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ef3e56c-18c2-47f8-876a-50c90f6b6c7d · outbound

This paper cites Yolov10-pose and yolov9-pose: Real-time strawberry stalk pose detection models,.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Yolov10-pose and yolov9-pose: Real-time strawberry stalk pose detection models,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:48:18.412304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4796692c-ed27-476c-84ac-2cfc3a694e98 · outbound

This paper cites Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities.

Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development Generative AI in Agriculture: Creating Image Datasets Using DALL.E's Advanced Large Language Model Capabilities

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T18:48:17.942486Z

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Unavailable: canonical work link unavailable.

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