papers

Publications (14)

cs.CV2024

CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation

Lingjun Zhao, Jingyu Song, Katherine A. Skinner

In the field of 3D object detection for autonomous driving, LiDAR-Camera (LC) fusion is the top-performing sensor configuration. Still, LiDAR is relatively high cost, which hinders…

cs.CV2025

MTSGL: Multi-Task Structure Guided Learning for Robust and Interpretable SAR Aircraft Recognition

Qishan He, Lingjun Zhao, Ru Luo +4

Aircraft recognition in synthetic aperture radar (SAR) imagery is a fundamental mission in both military and civilian applications. Recently deep learning (DL) has emerged a domina…

cs.CL2026

Pragmatics Meets Culture: Culturally-adapted Artwork Description Generation and Evaluation

Lingjun Zhao, Dayeon Ki, Marine Carpuat +1

Language models are known to exhibit various forms of cultural bias in decision-making tasks, yet much less is known about their degree of cultural familiarity in open-ended text g…

cs.CV2025

Can Hallucination Correction Improve Video-Language Alignment?

Lingjun Zhao, Mingyang Xie, Paola Cascante-Bonilla +2

Large Vision-Language Models often generate hallucinated content that is not grounded in its visual inputs. While prior work focuses on mitigating hallucinations, we instead explor…

cs.IR2020

Cross-lingual Information Retrieval with BERT

Zhuolin Jiang, Amro El-Jaroudi, William Hartmann +2

Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question…

cs.CV2026

ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding

Lingjun Zhao, Yandong Luo, James Hays +1

We introduce ShelfGaussian, an open-vocabulary multi-modal Gaussian-based 3D scene understanding framework supervised by off-the-shelf vision foundation models (VFMs). Gaussian-bas…

cs.CL2023

Hallucination Detection for Grounded Instruction Generation

Lingjun Zhao, Khanh Nguyen, Hal Daumé

We investigate the problem of generating instructions to guide humans to navigate in simulated residential environments. A major issue with current models is hallucination: they ge…

cs.CL2023

Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models

Lingjun Zhao, Khanh Nguyen, Hal Daumé

Recent work studies the cognitive capabilities of language models through psychological tests designed for humans. While these studies are helpful for understanding the general cap…

cs.CV2022

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

Yan Zhao, Lingjun Zhao, Zhong Liu +3

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearan…

cs.CV2025

Diffusion-Denoised Hyperspectral Gaussian Splatting

Sunil Kumar Narayanan, Lingjun Zhao, Lu Gan +1

Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise quantification of sample nutr…

cs.RO2024

LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection

Jingyu Song, Lingjun Zhao, Katherine A. Skinner

We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabili…

cs.CV2026

GaussianFormer3D: Multi-Modal Gaussian-based Semantic Occupancy Prediction with 3D Deformable Attention

Lingjun Zhao, Sizhe Wei, James Hays +1

3D semantic occupancy prediction is essential for achieving safe, reliable autonomous driving and robotic navigation. Compared to camera-only perception systems, multi-modal pipeli…

cs.CL2025

A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations

Lingjun Zhao, Hal Daumé

Faithful free-text explanations are important to ensure transparency in high-stakes AI decision-making contexts, but they are challenging to generate by language models and assess…

cs.AI2024

Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections

Lingjun Zhao, Khanh Nguyen, Hal Daumé

Language models will inevitably err in situations with which they are unfamiliar. However, by effectively communicating uncertainties, they can still guide humans toward making sou…