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Rethinking driving world model as synthetic data generator for perception tasks (100) 사진

2107.04259] Unity Perception: Generate Synthetic Data for Computer Vision

A GAN-Based Framework with Dynamic Adaptive Attention for Multi-Class Image Segmentation in Autonomous Driving

BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models - MarkTechPost

Generative AI Meets Computer Vision | API4AI | Medium

Flexible multitask deep learning for lane-aware panoptic segmentation in autonomous driving [PeerJ]

Review 2 Report........ | PDF | Computer Vision | Artificial Intelligence

Important LLM Papers for the Week From 10/11 To 16/11

Generate synthetic datasets and scenarios by learning from the real world

ICCV 2025 Boundary-Pushing CV Research | Voxel51

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

CMES | Free Full-Text | Deep Learning and Federated Learning in Human Activity Recognition with Sensor Data: A Comprehensive Review

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An interactive segmentation-based method for seismic facies annotation and segmentation | Scientific Reports

Important LLM Papers for the Week From 28/07 to 02/08

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Federated learning system on autonomous vehicles for lane segmentation | Scientific Reports

Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene

End-to-end Autonomous Driving: Challenges and Frontiers - Reading Collections

Foundation models and intelligent decision-making: Progress, challenges, and perspectives: The Innovation

Google AI Introduces Learn-by-Interact: A Data-Centric Framework for Adaptive and Efficient LLM Agent Development - MarkTechPost

Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

From Perception to Action: The Role of World Models in Embodied AI Systems - MarkTechPost

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Important LLM Papers for the Week From 10/11 To 16/11

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

The Art of Synthetic Wisdom: Teaching Machines to Think in the Absence of Data | by R. Thompson (PhD) | Towards AI

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Learning naturalistic driving environment with statistical realism | Nature Communications

PDF) SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration

Frontiers | A survey of decision-making and planning methods for self- driving vehicles

分享下我们团队最新工作Dream4Drive【小米汽车x北大】 - 知乎

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

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Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

PDF) Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Synthetic data generation for the continuous development ...

Important LLM Papers for the Week From 10/11 To 16/11

Large language models empowered agent-based modeling and simulation: a survey and perspectives | Humanities and Social Sciences Communications

Paper page - GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation

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A recent T-RO paper introduces a model-simplification framework for efficient deformable-object manipulation: task-conditioned action-space reduction plus simplified #dynamics lets faster planning for cloth folding & rope shaping. Paper title: Goal ...

Daily Papers - Hugging Face

A Critical Analysis of Generative AI: Challenges, Opportunities, and Future Research Directions | Archives of Computational Methods in Engineering | Springer Nature Link

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Papers by Bohan Zeng

Certified Robustness in Automated Driving Perception: A Review | Automotive Innovation

AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation

Synchronization, Optimization, and Adaptation of Machine Learning Techniques for Computer Vision in Cyber-Physical Systems: A Comprehensive Analysis[v2] | Preprints.org

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Multimodal Language Models in Autonomous Driving | PDF | Artificial Intelligence | Intelligence (AI) & Semantics

ICCV 2025 Boundary-Pushing CV Research | Voxel51

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Automated Prompt Engineering: Leveraging Synthetic Data and Meta-Prompts for Enhanced LLM Performance - MarkTechPost

Certified Robustness in Automated Driving Perception: A Review | Automotive Innovation

Foundation models and intelligent decision-making: Progress, challenges, and perspectives: The Innovation

Vision Language Models in Autonomous Driving: A Survey and Outlook

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

Synchronization, Optimization, and Adaptation of Machine Learning Techniques for Computer Vision in Cyber-Physical Systems: A Comprehensive Analysis[v2] | Preprints.org

Synthetic Data Generation Using Large Language Models: Advances in Text and Code

BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models - MarkTechPost

Large language models empowered agent-based modeling and simulation: a survey and perspectives | Humanities and Social Sciences Communications

Simulately

Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

This AI Research Introduces FollowNet: A Comprehensive Benchmark Dataset for Car-Following Behavior Modeling - MarkTechPost

PDF) WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

2312.00438] Dolphins: Multimodal Language Model for Driving

Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective

Important LLM Papers for the Week From 27/10 To 01/11

Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? (Google DeepMind, July 2024) Paper: [https://arxiv.org/abs/2406.13121](https://arxiv.org/abs/2406.13121) Abstract:

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Generative AI for Autonomous Driving: Frontiers and Opportunities

Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

publications | Aleksandr I. Panov

Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling (Shanghai AI Laboratory, February 2025) Paper: [https://arxiv.org/abs/2502.06703](https://arxiv.org/abs/2502.06703) Abstract:

🧠 Top 5 Trends in Data Annotation Heading Into 2026 In 2026, the focus is shifting from “more labels” to smarter, scalable, and production-ready data pipelines. Here are the five key data

Multi‐domain autonomous driving dataset: Towards enhancing the generalization of the convolutional neural networks in new environments - Khosravian - 2023 - IET Image Processing - Wiley Online Library

🧠 Top 5 Trends in Data Annotation Heading Into 2026 In 2026, the focus is shifting from “more labels” to smarter, scalable, and production-ready data pipelines. Here are the five key data

Unleashing the potential of prompt engineering for large language models: Patterns

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Robust motion planning for mobile robots under attacks against obstacle localization | Robotica | Cambridge Core

Generate synthetic datasets and scenarios by learning from the real world

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Vision tasks in clear and rain-augmented images. Our synthetic rain... | Download Scientific Diagram

OS-Genesis: A Novel GUI Data Synthesis Pipeline that Reverses the Conventional Trajectory Collection Process - MarkTechPost

Road surface semantic segmentation for autonomous driving [PeerJ]

4x RGT_S - OpenModelDB

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

PDF) Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities

Road surface semantic segmentation for autonomous driving [PeerJ]

Generative AI in depth: A survey of recent advances, model variants, and real-world applications | Journal of Big Data | Springer Nature Link

Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?

CV计算机视觉每日开源代码Paper with code速览-2025.11.15 - 知乎

Important LLM Papers for the Week From 27/10 To 01/11

Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems | JACC: Advances

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