XLM for Autonomous Driving Systems: A Comprehensive Review
Large Language Models (LLMs) have showcased remarkable proficiency in various
information-processing tasks. These tasks span from extracting data and summarizing …
information-processing tasks. These tasks span from extracting data and summarizing …
Fastsam3d: An efficient segment anything model for 3d volumetric medical images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization
capability in segmenting objects of unseen classes and in unseen domains when properly …
capability in segmenting objects of unseen classes and in unseen domains when properly …
A decision-making model for self-driving vehicles based on GPT-4V, federated reinforcement learning, and blockchain
Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly
influence future transportation systems. Observing the current driving status of autonomous …
influence future transportation systems. Observing the current driving status of autonomous …
[PDF][PDF] LLM for Differentiable Surface Sampling for Masked Modeling on Point Clouds
Z Wang, W Sun, ZC Chu, Y Zhang, Z Wu - 2024 - allmultidisciplinaryjournal.com
We present MaskPOINT, a novel scheme of masked-wise pretrained models for point cloud
self-supervised learning, addressing the challenges posed by 3D understanding, including …
self-supervised learning, addressing the challenges posed by 3D understanding, including …
Vision and structured-language pretraining for cross-modal food retrieval
Abstract Vision-Language Pretraining (VLP) and Foundation models have been the go-to
recipe for achieving SoTA performance on general benchmarks. However, leveraging these …
recipe for achieving SoTA performance on general benchmarks. However, leveraging these …
Tinysam-med3d: A lightweight segment anything model for volumetric medical imaging with mixture of experts
T Song, G Kang, Y Shen - … Conference on Artificial Intelligence in Medicine, 2024 - Springer
Segment anything models (SAMs) demonstrate exceptional zero-shot segmentation
capabilities on natural images when provided appropriate prompts. However, directly …
capabilities on natural images when provided appropriate prompts. However, directly …
Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models
Y Yang, Y Hu, M Ye, Z Zhang, Z Lu, Y Xu… - arXiv preprint arXiv …, 2024 - arxiv.org
Multimodal foundation models offer promising advancements for enhancing driving
perception systems, but their high computational and financial costs pose challenges. We …
perception systems, but their high computational and financial costs pose challenges. We …
Uniform Transformation: Refining Latent Representation in Variational Autoencoders
Irregular distribution in latent space causes posterior collapse, misalignment between
posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this …
posterior and prior, and ill-sampling problem in Variational Autoencoders (VAEs). In this …
Segment Anything Model for Zero-shot Single Particle Tracking in Liquid Phase Transmission Electron Microscopy
Liquid phase transmission electron microscopy (LPTEM) offers an unparalleled combination
of spatial and temporal resolution, making it a promising tool for single particle tracking at …
of spatial and temporal resolution, making it a promising tool for single particle tracking at …
Improving 3D Object Detection for Autonomous Driving–A Case Study of Data-Driven Development
A Hartmannsgruber, C Pinke, C Jing… - International ATZ …, 2024 - Springer
Autonomous Driving (AD) solutions are poised to revolutionize mobility, driving significant
R&D efforts. However, scaling this technology presents major challenges, necessitating a re …
R&D efforts. However, scaling this technology presents major challenges, necessitating a re …