100 citations · 395 across the 34 of their papers we have counts for
34 papers
GeoWAM: Visual Geometry World Action Models for Autonomous Driving
Yiren Lu, Xin Ye, Jiaming Liu +10
World action models (WAMs) have recently gained increasing attention as a framework for jointly modeling scene evolution and ego actions in autonomous driving. Most existing WAMs l…
TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval
Yi-Chung Chen, Philip Jacobson, Tom Lampo +6
Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval syst…
VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving
Thach Nguyen, Danhua Guo, Tom Lampo +2
Vision-language-action (VLA) models generate chain-of-thought (CoT) reasoning alongside driving trajectories, but existing benchmarks evaluate only trajectory quality and do not as…
VLGA: Vision-Language-Geometry-Action Models for Autonomous Driving
Jin Yao, Dhruva Dixith Kurra, Tom Lampo +3
Vision-language-action (VLA) models can describe scenes and reason about them in language, yet still struggle to ground their actions in the dense 3D world around them. Existing ap…
Reconstruction Matters: Learning Geometry-Aligned BEV Representation through 3D Gaussian Splatting
Yiren Lu, Xin Ye, Burhaneddin Yaman +4
Bird's-Eye-View (BEV) perception serves as a cornerstone for autonomous driving, offering a unified spatial representation that fuses surrounding-view images to enable reasoning fo…
LTDA-Drive: LLMs-guided Generative Models based Long-tail Data Augmentation for Autonomous Driving
Mahmut Yurt, Xin Ye, Yunsheng Ma +5
3D perception plays an essential role for improving the safety and performance of autonomous driving. Yet, existing models trained on real-world datasets, which naturally exhibit l…
ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving
Yunsheng Ma, Burhaneddin Yaman, Xin Ye +5
Recent advances have explored integrating large language models (LLMs) into end-to-end autonomous driving systems to enhance generalization and interpretability. However, most exis…
BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance
Xin Ye, Burhaneddin Yaman, Sheng Cheng +3
Bird's-eye-view (BEV) representations play a crucial role in autonomous driving tasks. Despite recent advancements in BEV generation, inherent noise, stemming from sensor limitatio…
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Hang Wang, Xin Ye, Feng Tao +5
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning polic…
MTA: Multimodal Task Alignment for BEV Perception and Captioning
Yunsheng Ma, Burhaneddin Yaman, Xin Ye +5
Bird's eye view (BEV)-based 3D perception plays a crucial role in autonomous driving applications. The rise of large language models has spurred interest in BEV-based captioning to…
LORD: Large Models based Opposite Reward Design for Autonomous Driving
Xin Ye, Feng Tao, Abhirup Mallik +2
Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However, crafting effective reward functio…
PaPr: Training-Free One-Step Patch Pruning with Lightweight ConvNets for Faster Inference
Tanvir Mahmud, Burhaneddin Yaman, Chun-Hao Liu +1
As deep neural networks evolve from convolutional neural networks (ConvNets) to advanced vision transformers (ViTs), there is an increased need to eliminate redundant data for fast…
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow
Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar +1
Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in int…
VLP: Vision Language Planning for Autonomous Driving
Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti +4
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have…
Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models
Valentino Assandri, Sam Heshmati, Burhaneddin Yaman +2
Deep learning models, particularly Transformers, have achieved impressive results in various domains, including time series forecasting. While existing time series literature prima…
SSVOD: Semi-Supervised Video Object Detection with Sparse Annotations
Tanvir Mahmud, Chun-Hao Liu, Burhaneddin Yaman +1
Despite significant progress in semi-supervised learning for image object detection, several key issues are yet to be addressed for video object detection: (1) Achieving good perfo…
Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection
Burhaneddin Yaman, Tanvir Mahmud, Chun-Hao Liu
We propose an embarrassingly simple method -- instance-aware repeat factor sampling (IRFS) to address the problem of imbalanced data in long-tailed object detection. Imbalanced dat…
Object Detection for Autonomous Dozers
Chun-Hao Liu, Burhaneddin Yaman
We introduce a new type of autonomous vehicle - an autonomous dozer that is expected to complete construction site tasks in an efficient, robust, and safe manner. To better handle…
Accelerated MRI With Deep Linear Convolutional Transform Learning
Hongyi Gu, Burhaneddin Yaman, Steen Moeller +2
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple application…
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman +4
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.…
End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction
Ruiyang Zhao, Yuxin Zhang, Burhaneddin Yaman +2
Deep learning techniques have emerged as a promising approach to highly accelerated MRI. However, recent reconstruction challenges have shown several drawbacks in current deep lear…
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang +7
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medica…
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved…
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions.…