Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems | Paper Citation Record

Observation 9024ef81-34ab-4c93-9f2a-dd7f993dcae3 · outbound

This paper cites Vision Language Models in Autonomous Driving: A Survey and Outlook.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Vision Language Models in Autonomous Driving: A Survey and Outlook

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This paper cites Multi-modal Sensor Fusion for Auto Driving Perception: A Survey.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

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Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Mask r-cnn,

Reference 4

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 5

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This paper cites Segmenter: Transformer for semantic segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Segmenter: Transformer for semantic segmentation,

Reference 6

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This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 7

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This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Bdd100k: A diverse driving dataset for heterogeneous multitask learning,

Reference 8

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This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Encoder-decoder with atrous separable convolution for semantic image segmentation,

Reference 9

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This paper cites Panoptic segmenta- tion,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Panoptic segmenta- tion,

Reference 10

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This paper cites Deep learning for 3d point clouds: A survey,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Deep learning for 3d point clouds: A survey,

Reference 11

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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This paper cites nuscenes: A multimodal dataset for autonomous driv- ing,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems nuscenes: A multimodal dataset for autonomous driv- ing,

Reference 13

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Observation e2924f5b-bd53-4950-88ab-9b6b9c948f3e · outbound

This paper cites The mapillary vistas dataset for semantic under- standing of street scenes,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems The mapillary vistas dataset for semantic under- standing of street scenes,

Reference 14

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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This paper cites Language Models are Few-Shot Learners.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Language Models are Few-Shot Learners

Reference 16

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This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 17

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This paper cites Learning transferable visual models from natural language supervision,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Learning transferable visual models from natural language supervision,

Reference 18

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Observation 8af6236e-1345-42fc-8a1c-dc6f41a1c960 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems DINOv2: Learning Robust Visual Features without Supervision

Reference 19

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This paper cites Segment Everything Everywhere All at Once.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Segment Everything Everywhere All at Once

Reference 21

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Observation 5c83ba3e-afba-491e-8b4e-1b52f9ac3ba7 · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 23

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This paper cites Traffic scene perception via multimodal large language model with data augmentation and efficient training strategy,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Traffic scene perception via multimodal large language model with data augmentation and efficient training strategy,

Reference 24

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This paper cites Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets

Reference 25

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This paper cites Traffic scene analysis using vision-language models,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Traffic scene analysis using vision-language models,

Reference 26

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This paper cites Talk2car: Taking control of your self-driving car,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Talk2car: Taking control of your self-driving car,

Reference 27

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This paper cites Existence and uniqueness of solutions in the Lipschitz space of a functional equation and its application to the behavior of the paradise fish.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Existence and uniqueness of solutions in the Lipschitz space of a functional equation and its application to the behavior of the paradise fish

Reference 28

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This paper cites Image segmentation using text and image prompts,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Image segmentation using text and image prompts,

Reference 29

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Observation af70fe27-9875-43e5-88b8-0bc3e1f39d3b · outbound

This paper cites Scaling open-vocabulary image segmentation with image-level labels,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Scaling open-vocabulary image segmentation with image-level labels,

Reference 30

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This paper cites Segment Anything.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Segment Anything

Reference 31

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Observation edcffdb4-ca86-4466-99ac-1d075f5b6d08 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 32

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Observation 36d2752c-e765-4263-b4e8-a1a24f05caab · outbound

This paper cites EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers

Reference 33

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Observation 09603f13-1ec2-43cb-9bbb-d392a7225a02 · outbound

This paper cites Robust image classification with multi- modal large language models,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Robust image classification with multi- modal large language models,

Reference 34

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This paper cites Driving forward: Semantic segmenta- tion in autonomous vehicles,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Driving forward: Semantic segmenta- tion in autonomous vehicles,

Reference 36

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This paper cites Real-time semantic segmentation for autonomous driving: A review of cnns, transformers, and beyond,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Real-time semantic segmentation for autonomous driving: A review of cnns, transformers, and beyond,

Reference 37

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This paper cites Peculiarities of the chemical enrichment of metal-poor Stars in the Milky Way Galaxy.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Peculiarities of the chemical enrichment of metal-poor Stars in the Milky Way Galaxy

Reference 38

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Observation e6dc2a03-451a-4f51-b52c-b8cf2d16ca82 · outbound

This paper cites Invariant tori and boundedness of solutions of non-smooth oscillators with Lebesgue integrable forcing term.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Invariant tori and boundedness of solutions of non-smooth oscillators with Lebesgue integrable forcing term

Reference 39

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Observation 62029bee-d5da-4d03-9b51-aaf310f1f310 · outbound

This paper cites Available: https://www.keylabs.ai/blog/ driving-forward-semantic-segmentation-in-autonomous-vehicles/.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Available: https://www.keylabs.ai/blog/ driving-forward-semantic-segmentation-in-autonomous-vehicles/

Reference 40

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This paper cites Oneformer: One transformer to rule them all for universal image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Oneformer: One transformer to rule them all for universal image segmentation,

Reference 41

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This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 42

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This paper cites Fully convolutional networks for semantic segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Fully convolutional networks for semantic segmentation,

Reference 43

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This paper cites Masked-attention mask transformer for universal im- age segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Masked-attention mask transformer for universal im- age segmentation,

Reference 44

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This paper cites U-net: Convolutional net- works for biomedical image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems U-net: Convolutional net- works for biomedical image segmentation,

Reference 45

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Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Normalized cuts and image segmentation,

Reference 46

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This paper cites Cross-modal self-attention network for referring image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Cross-modal self-attention network for referring image segmentation,

Reference 47

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This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 48

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Observation 2b306609-5358-43cb-87a1-adb64d6b1592 · outbound

This paper cites Phrasecut: Language-based image segmentation in the wild,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Phrasecut: Language-based image segmentation in the wild,

Reference 49

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This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 50

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This paper cites Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Vilbert: Pretraining task- agnostic visiolinguistic representations for vision-and-language tasks,

Reference 51

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This paper cites Bi-directional re- lationship inferring network for referring image segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Bi-directional re- lationship inferring network for referring image segmentation,

Reference 52

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Observation 73241321-e5a6-4e07-abfe-bb75f5882e26 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Imagenet classification with deep convolutional neural networks,

Reference 53

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Observation 4958ea0c-5684-4a7f-a240-f252f6a57d3e · outbound

This paper cites Refvos: A closer look at referring expressions for video object segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Refvos: A closer look at referring expressions for video object segmentation,

Reference 54

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Observation d6e573bc-74c0-45ee-a36d-d3fb5b3cc343 · outbound

This paper cites Attention is all you need,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Attention is all you need,

Reference 55

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Observation 8d43780a-2e77-4301-b798-b1e969882036 · outbound

This paper cites Lxmert: Learning cross-modality encoder rep- resentations from transformers,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Lxmert: Learning cross-modality encoder rep- resentations from transformers,

Reference 56

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Observation e0eb2a2a-f75a-48e2-9b50-68f954a35086 · outbound

This paper cites LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

Reference 57

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 58

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Observation 54cbab60-a88e-4aed-8fb7-545a0c911fdf · outbound

This paper cites Overspinning a rotating black hole in semiclassical gravity with type-A trace anomaly.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Overspinning a rotating black hole in semiclassical gravity with type-A trace anomaly

Reference 59

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Observation e2953afd-f3b6-477a-bcfa-7b0d7b045835 · outbound

This paper cites Attention Is All You Need.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Attention Is All You Need

Reference 60

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Observation 6684055d-7d0f-41ae-ad8a-cdd2db99af1b · outbound

This paper cites Semantic segmentation datasets for autonomous driving,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Semantic segmentation datasets for autonomous driving,

Reference 61

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Observation 9885fcc2-eb39-40a3-bc44-d7844e31e5c4 · outbound

This paper cites DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.439085Z

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Observation 254657f6-bd64-4f14-94d3-bf0f2c727af9 · outbound

This paper cites A Survey on Multimodal Large Language Models for Autonomous Driving.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems A Survey on Multimodal Large Language Models for Autonomous Driving

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.421328Z

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Observation bfd87f47-d46b-41e4-8a7a-f2d0ad3e11e2 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.446800Z

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Observation 3c9e90d3-4a07-44b4-9a1b-20674f6aa6a0 · outbound

This paper cites Mean-field and cumulant approaches to modelling organic polariton physics.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Mean-field and cumulant approaches to modelling organic polariton physics

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.429947Z

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Observation 1e2acdb4-0269-44ab-889c-c05328cfb671 · outbound

This paper cites XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.433822Z

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Observation cac7fad4-be08-47b2-9ad5-30d6b9f0b730 · outbound

This paper cites Efficient unstructured pruning of mamba state-space models for resource-constrained environments,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Efficient unstructured pruning of mamba state-space models for resource-constrained environments,

Reference 67

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:58:01.866581Z

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Observation f6b1fb5a-955f-41dd-a0fb-bcdb5043e826 · outbound

This paper cites WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:58:01.883605Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7c30b918-481c-45d1-bfa3-525b5b07766d · outbound

This paper cites GPT Understands, Too.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems GPT Understands, Too

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.468134Z

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Observation e9c754aa-97bf-43c3-b9da-318f88211f36 · outbound

This paper cites Deep residual learning for image recognition,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Deep residual learning for image recognition,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.450695Z

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Observation 3f4d3675-8176-4d76-9df9-7a809b33d044 · outbound

This paper cites Efficientvit: Memory efficient vision transformer with cascaded group attention,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Efficientvit: Memory efficient vision transformer with cascaded group attention,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.455566Z

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Observation fc964efb-ffd4-4742-8326-6e62fa99ccc7 · outbound

This paper cites Multimodal compact bilinear pooling for visual ques- tion answering and visual grounding,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Multimodal compact bilinear pooling for visual ques- tion answering and visual grounding,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.479020Z

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Observation 313c5e9f-78f6-409b-95d2-aade2de02de8 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.464208Z

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Observation 4701650d-827b-4e98-8b9d-2bb1acff2070 · outbound

This paper cites Unifying Vision-and-Language Tasks via Text Generation.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Unifying Vision-and-Language Tasks via Text Generation

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.486496Z

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Observation 453c9653-25de-4a8b-a3a6-b05e1c1dd0c6 · outbound

This paper cites Unify, align and refine: A unified framework for vision-and-language pre-training,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Unify, align and refine: A unified framework for vision-and-language pre-training,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.471775Z

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Observation 93b2528d-53cc-401b-983c-3b008f100bc6 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.475144Z

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Observation fd92956f-e1d5-4be3-8dbc-831c488787e0 · outbound

This paper cites Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Precise and Robust Sidewalk Detection: Leveraging Ensemble Learning to Surpass LLM Limitations in Urban Environments

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:58:01.706727Z

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Observation 9709f0da-de33-451f-9fe9-94b96b7e65f9 · outbound

This paper cites Bilinear attention net- works,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Bilinear attention net- works,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.482776Z

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Observation a35832fb-3e71-4604-a6d0-cd21b98f960b · outbound

This paper cites Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.505857Z

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Observation fe95bcdc-6188-4d4a-94c6-7ae2f67b6949 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Improved Baselines with Visual Instruction Tuning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.490113Z

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Observation bc203a75-bf37-407e-95a1-63c658c2f0dc · outbound

This paper cites LISA: Reasoning Segmentation via Large Language Model.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems LISA: Reasoning Segmentation via Large Language Model

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.493813Z

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Observation 937822cb-63f4-401c-97df-a274ed196983 · outbound

This paper cites Optimal Qubit Reuse for Near-Term Quantum Computers.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Optimal Qubit Reuse for Near-Term Quantum Computers

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:58:01.530270Z

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Observation d6c172a5-485e-484c-92ea-f33e9f928537 · outbound

This paper cites Clearvision: Leveraging cyclegan and siglip-2 for robust all- weather classification in traffic camera imagery,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Clearvision: Leveraging cyclegan and siglip-2 for robust all- weather classification in traffic camera imagery,

Reference 83

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:58:01.688409Z

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Observation 63737ba6-55a8-4212-b1b5-55039a8e4b78 · outbound

This paper cites Indiscernibles and satisfaction classes in arithmetic.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Indiscernibles and satisfaction classes in arithmetic

Reference 84

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:58:01.503885Z

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Observation 35631a85-ca93-4d54-b6e3-6c38204dcdc1 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.510086Z

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Observation 08af4a26-a045-41e7-815f-37b3c45d3576 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.513878Z

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Observation bb5bfc43-fed8-4fc9-a1dc-3f46bb210ee4 · outbound

This paper cites Communication-efficient learning of deep networks from decentral- ized data,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Communication-efficient learning of deep networks from decentral- ized data,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.539234Z

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Observation 94960be8-8710-40b6-a687-c7f5fb99288b · outbound

This paper cites Clip2scene: Towards label-efficient 3d scene understanding by clip,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Clip2scene: Towards label-efficient 3d scene understanding by clip,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.522947Z

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Observation dd2c064d-8450-4298-9b70-1e19fd8449c4 · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Federated learning: Challenges, methods, and future directions,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.546211Z

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Observation 252ce833-a51e-4bb0-bcd9-c49496be9667 · outbound

This paper cites UniVS: Unified and Universal Video Segmentation with Prompts as Queries.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems UniVS: Unified and Universal Video Segmentation with Prompts as Queries

Reference 90

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:58:01.484159Z

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Observation f439cf81-3bb4-47ca-a43c-43af95a9b196 · outbound

This paper cites Video object segmentation: A survey,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Video object segmentation: A survey,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.535726Z

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Observation f093e63f-f03e-4fb6-92f8-4d3c32d97d8c · outbound

This paper cites Towards explainable traffic flow prediction with large language models,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Towards explainable traffic flow prediction with large language models,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.557443Z

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Observation 7eec3855-938b-4d41-b691-4a51b50e53b6 · outbound

This paper cites Advances and open problems in federated learning,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Advances and open problems in federated learning,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.542692Z

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Observation b4b3495f-b751-48e2-8c80-0686d580c2a9 · outbound

This paper cites A review of deep learning- based methods for pavement defect detection,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems A review of deep learning- based methods for pavement defect detection,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:02.512999Z

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Observation 7c7e5b9b-387b-4c5c-8125-95f033617598 · outbound

This paper cites General derivative Thomae formula for singular half-periods.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems General derivative Thomae formula for singular half-periods

Reference 95

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:58:01.464460Z

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Observation 22ef35fa-78d8-4675-9e7b-b1b6c0ca6cd8 · outbound

This paper cites Cooper: A query-based collaborative perception framework for 3d object detection,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Cooper: A query-based collaborative perception framework for 3d object detection,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.553880Z

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Observation 1ce0542f-456c-4e8b-83b5-2b32e23f2bf7 · outbound

This paper cites Topological frequency conversion in rhombohedral multilayer graphene.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Topological frequency conversion in rhombohedral multilayer graphene

Reference 97

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:58:01.362235Z

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Observation dfc44bcc-ce4d-435b-b010-58eb2323482e · outbound

This paper cites Robust and precise sidewalk detection with ensemble learning: Enhancing road safety and facilitating curb space management,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Robust and precise sidewalk detection with ensemble learning: Enhancing road safety and facilitating curb space management,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.562218Z

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Observation b11aa495-8c5c-49e0-b22a-e201d0125a27 · outbound

This paper cites Evaluate PAC codes via Efficient Estimation on Weight Distribution.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Evaluate PAC codes via Efficient Estimation on Weight Distribution

Reference 99

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:58:01.329276Z

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Observation 28207b59-6aab-4c8b-94e5-76f081eef182 · outbound

This paper cites Road pothole detection and classification using deep convolutional neu- ral networks,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Road pothole detection and classification using deep convolutional neu- ral networks,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:02.500592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1d5db5cb-ccb8-40ef-a346-232fdf2d0157 · outbound

This paper cites Lingo-1: A foundation model for language-driven autonomous vehicles,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Lingo-1: A foundation model for language-driven autonomous vehicles,

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:58:02.486793Z

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Observation c5f67be0-bad6-48d9-86b9-a8e8e084c486 · outbound

This paper cites Talk2bev: Language- grounded bird’s-eye-view for autonomous driving,.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems Talk2bev: Language- grounded bird’s-eye-view for autonomous driving,

Reference 102

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:58:01.265524Z

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Observation 23935f2c-e0a4-4d53-b27e-19e63b15e906 · outbound

This paper cites LMDrive: Closed-Loop End-to-End Driving with Large Language Models.

Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems LMDrive: Closed-Loop End-to-End Driving with Large Language Models

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-15T19:58:00.586091Z

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