Publications (34)
DenseBox: Unifying Landmark Localization with End to End Object Detection
Lichao Huang, Yi Yang, Yafeng Deng +1
How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding box…
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
Naiyu Gao, Yanhu Shan, Yupei Wang +4
Recently, proposal-free instance segmentation has received increasing attention due to its concise and efficient pipeline. Generally, proposal-free methods generate instance-agnost…
KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Shuai Wang, Yinan Yu
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive reasoning. Structured Knowledge G…
Domain-Invariant Prompt Learning for Vision-Language Models
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Large pre-trained vision-language models like CLIP have transformed computer vision by aligning images and text in a shared feature space, enabling robust zero-shot transfer via pr…
Cost-Aware Prediction (CAP): An LLM-Enhanced Machine Learning Pipeline and Decision Support System for Heart Failure Mortality Prediction
Yinan Yu, Falk Dippel, Christina E. Lundberg +4
Objective: Machine learning (ML) predictive models are often developed without considering downstream value trade-offs and clinical interpretability. This paper introduces a cost-a…
Tapping in a Remote Vehicle's onboard LLM to Complement the Ego Vehicle's Field-of-View
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu +1
Today's advanced automotive systems are turning into intelligent Cyber-Physical Systems (CPS), bringing computational intelligence to their cyber-physical context. Such systems pow…
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
Large Language Models (LLMs) excel in many natural language processing tasks but often exhibit factual inconsistencies in knowledge-intensive settings. Integrating external knowled…
Domain Generalization through Meta-Learning: A Survey
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inev…
Building Efficient CNNs Using Depthwise Convolutional Eigen-Filters (DeCEF)
Yinan Yu, Samuel Scheidegger, Tomas McKelvey
Deep Convolutional Neural Networks (CNNs) have been widely used in various domains due to their impressive capabilities. These models are typically composed of a large number of 2D…
DomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
Shuai Wang, Dhasarathy Parthasarathy, Robert Feldt +1
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-…
Trajectory-guided discharge stratification for heart failure using short-context electronic health record sequence modeling
Falk Dippel, Yinan Yu, Annika Rosengren +6
Purpose: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health…
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making
Jingchu Liu, Pengfei Hou, Lisen Mu +2
Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical comp…
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Shuai Wang, Yinan Yu
Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KB…
Scalability in Building Component Data Annotation: Enhancing Facade Material Classification with Synthetic Data
Josie Harrison, Alexander Hollberg, Yinan Yu
Computer vision models trained on Google Street View images can create material cadastres. However, current approaches need manually annotated datasets that are difficult to obtain…
LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu +1
Today's Large Language Models (LLMs) have showcased exemplary capabilities, ranging from simple text generation to advanced image processing. Such models are currently being explor…
Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Shuai Wang, Xixi Wang, Yinan Yu
Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question An…
Latent Domain Prompt Learning for Vision-Language Models
Zhixing Li, Arsham Gholamzadeh Khoee, Yinan Yu
The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applicati…
Polymer: Development Workflows as Software
Dhasarathy Parthasarathy, Yinan Yu, Earl T. Barr
Software development builds digital tools to automate processes, yet its initial phases, up to deployment, remain largely manual. There are two reasons: Development tasks are often…
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
Yiyi Liao, Lichao Huang, Yue Wang +3
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing c…
AirDnD -- Asynchronous In-Range Dynamic and Distributed Network Orchestration Framework
Malsha Ashani Mahawatta Dona, Christian Berger, Yinan Yu
The increasing usage of IoT devices has generated an extensive volume of data which resulted in the establishment of data centers with well-structured computing infrastructure. Red…
Automating a Complete Software Test Process Using LLMs: An Automotive Case Study
Shuai Wang, Yinan Yu, Robert Feldt +1
Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control v…
Review Helpfulness Scores vs. Review Unhelpfulness Scores: Two Sides of the Same Coin or Different Coins?
Yinan Yu, Dominik Gutt, Warut Khern-am-nuai
Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review eval…
A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure
Yinan Yu, Samuel Scheidegger, John-Fredrik Grönvall +4
Single-vehicle accidents are the most common type of fatal accidents in Sweden, where a car drives off the road and runs into hazardous roadside objects. Proper installation and ma…
GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt +3
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to…
PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles
Yinan Yu, Samuel Scheidegger
Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconci…
Arm locking for space-based laser interferometry gravitational wave observatories
Yinan Yu, Shawn Mitryk, Guido Mueller
Laser frequency stabilization is a critical part of the interferometry measurement system of space-based gravitational wave observatories such as the Laser Interferometer Space Ant…
BetterCheck: Towards Safeguarding VLMs for Automotive Perception Systems
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu +1
Large language models (LLMs) are growingly extended to process multimodal data such as text and video simultaneously. Their remarkable performance in understanding what is shown in…
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP
Zhixing Li, Yinan Yu
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that a…
Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks
Malsha Ashani Mahawatta Dona, Beatriz Cabrero-Daniel, Yinan Yu +1
Today's advanced driver assistance systems (ADAS), like adaptive cruise control or rear collision warning, are finding broader adoption across vehicle classes. Integrating such adv…
Learning Hierarchical Feature Space Using CLAss-specific Subspace Multiple Kernel -- Metric Learning for Classification
Yinan Yu, Tomas McKelvey
Metric learning for classification has been intensively studied over the last decade. The idea is to learn a metric space induced from a normed vector space on which data from diff…
Deep Learning-based Scalable Image-to-3D Facade Parser for Generating Thermal 3D Building Models
Yinan Yu, Alex Gonzalez-Caceres, Samuel Scheidegger +2
Renovating existing buildings is essential for climate impact. Early-phase renovation planning requires simulations based on thermal 3D models at Level of Detail (LoD) 3, which inc…
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
Arsham Gholamzadeh Khoee, Shuai Wang, Robert Feldt +2
Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domai…
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review
Pierre Lamart, Yinan Yu, Christian Berger
Machine Learning (ML) is continuously permeating a growing amount of application domains. Generative AI such as Large Language Models (LLMs) also sees broad adoption to process mul…
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
Shuai Wang, Yinan Yu, Earl Barr +1
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI co…