mlsanityData to 14 Oct 2026

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

arXiv 2506.14096 ↗First posted 17 Jun 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

The integration of Large Language Models (LLMs) with computer vision is profoundly transforming perception tasks like image segmentation. For intelligent transportation systems (ITS), where accurate scene understanding is critical for safety and efficiency, this new paradigm offers unprecedented capabilities. This survey systematically reviews the emerging field of LLM-augmented image segmentation, focusing on its applications, challenges, and future directions within ITS. We provide a taxonomy of current approaches based on their prompting mechanisms and core architectures, and we highlight how these innovations can enhance road scene understanding for autonomous driving, traffic monitoring, and infrastructure maintenance. Finally, we identify key challenges, including real-time performance and safety-critical reliability, and outline a perspective centered on explainable, human-centric AI as a prerequisite for the successful deployment of this technology in next-generation transportation systems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 98 of its 151 references that are in the map. First posted on arXiv 17 Jun 2025.

Builds on

98 of 151 references.

References · 98

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  5. 2020Language Models are Few-Shot Learnerscited by 32,419
  6. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  7. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  8. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  9. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  10. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  11. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  12. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
38 more
  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2023Segment Anythingcited by 8,234
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2017Mask R-CNNcited by 7,277
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  7. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  8. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  10. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  11. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  12. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  13. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  14. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  15. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  16. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  17. 2019Advances and Open Problems in Federated Learningcited by 3,046
  18. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  19. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  20. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  21. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  22. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  23. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,553
  24. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  25. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  26. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  27. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  28. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  29. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  30. 2018Panoptic Segmentationcited by 802
  31. 2023LISA: Reasoning Segmentation via Large Language Modelcited by 776
  32. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  33. 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 637
  34. 2020Transformer Interpretability Beyond Attention Visualizationcited by 627
  35. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  36. 2020A Survey of Deep Active Learningcited by 554
  37. 2023Segment Everything Everywhere All at Oncecited by 548
  38. 2021Image Segmentation Using Text and Image Promptscited by 512

50 most cited of 98 All 98 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025LOSC: LiDAR Open-voc Segmentation Consolidatorcited by 5
  2. 2026SENSE: Stereo OpEN Vocabulary SEmantic Segmentationcited by 0