5 citations · 7 across the 16 of their papers we have counts for
16 papers
Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Zhenxin Li, Nadine Chang, Xinglong Sun +8
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implaus…
Test-Time Coverage: Test-Conditioned Data Curation for Deployment-Aware Learning
Nadine Chang, Maying Shen, Shizhe Diao +6
Deployed AI systems are often trained from broad candidate data pools, necessitating data curation towards the deployment test distribution. However, standard data curation methods…
From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence
Nadine Chang, Maying Shen, Shizhe Diao +6
We propose a language representation for multimodal data in which any observation, whether image, video, or text, is expressed as a bag of atomic propositions, simple statements ab…
Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
Nadine Chang, Maying Shen, Jialiang Wang +2
Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-system maintenance pipelines use a reactive…
Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
Tolga Dimlioglu, Nadine Chang, Maying Shen +2
Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, the training data, must address…
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Zhenxin Li, Nadine Chang, Wenhao Yao +9
Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for long-tail and safety-critical scenarios…
DriveCritic: Towards Context-Aware, Human-Aligned Evaluation for Autonomous Driving with Vision-Language Models
Jingyu Song, Zhenxin Li, Shiyi Lan +6
Benchmarking autonomous driving planners to align with human judgment remains a critical challenge, as state-of-the-art metrics like the Extended Predictive Driver Model Score (EPD…
Mitigating Multimodal Hallucinations via Gradient-based Self-Reflection
Shan Wang, Maying Shen, Nadine Chang +3
Multimodal large language models achieve strong performance across diverse tasks but remain prone to hallucinations, where outputs are not grounded in visual inputs. This issue can…
AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training
Feiyang Kang, Nadine Chang, Maying Shen +4
The computational burden and inherent redundancy of large-scale datasets challenge the training of contemporary machine learning models. Data pruning offers a solution by selecting…
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Zhenxin Li, Wenhao Yao, Zi Wang +7
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory s…
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
Jenny Schmalfuss, Nadine Chang, Vibashan VS +3
Vision language models (VLMs) respond to user-crafted text prompts and visual inputs, and are applied to numerous real-world problems. VLMs integrate visual modalities with large l…
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
Shihao Wang, Zhiding Yu, Xiaohui Jiang +6
The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabil…
Enhancing Autonomous Driving Safety with Collision Scenario Integration
Zi Wang, Shiyi Lan, Xinglong Sun +4
Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits th…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…
SSE: Multimodal Semantic Data Selection and Enrichment for Industrial-scale Data Assimilation
Maying Shen, Nadine Chang, Sifei Liu +1
In recent years, the data collected for artificial intelligence has grown to an unmanageable amount. Particularly within industrial applications, such as autonomous vehicles, model…
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
Shihao Wang, Zhiding Yu, Xiaohui Jiang +6
The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabil…