Qing Lian

According to our database1, Qing Lian authored at least 32 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models.
CoRR, May, 2026

Toward Deep Representation Learning for Event-Enhanced Visual Autonomous Perception: the eAP Dataset.
CoRR, March, 2026

Toward Deep Representation Learning for Event-Enhanced Visual Autonomous Perception: The eAP Dataset.
IEEE Trans. Robotics, 2026

2025
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning.
J. Mach. Learn. Res., 2025

Learning Better Representations for Crowded Pedestrians in Offboard LiDAR-Camera 3D Tracking-by-detection.
Proceedings of the IEEE International Conference on Robotics and Automation, 2025

Towards Generalizable Multi-Camera 3D Object Detection via Perspective Rendering.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
OmniBooth: Learning Latent Control for Image Synthesis with Multi-modal Instruction.
CoRR, 2024

SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs.
CoRR, 2024

The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs.
CoRR, 2024

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance.
CoRR, 2024

R-Tuning: Instructing Large Language Models to Say 'I Don't Know'.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

The Instinctive Bias: Spurious Images lead to Illusion in MLLMs.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions.
CoRR, 2023

Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing.
CoRR, 2023

MEDL-U: Uncertainty-aware 3D Automatic Annotator based on Evidential Deep Learning.
CoRR, 2023

Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF.
CoRR, 2023

Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking.
Proceedings of the Conference on Robot Learning, 2023

2022
Weakly Supervised Disentangled Generative Causal Representation Learning.
J. Mach. Learn. Res., 2022

MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones.
CoRR, 2022

Semi-supervised Monocular 3D Object Detection by Multi-view Consistency.
Proceedings of the Computer Vision - ECCV 2022, 2022

Exploring Geometric Consistency for Monocular 3D Object Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms.
CoRR, 2021

Geometry-aware data augmentation for monocular 3D object detection.
CoRR, 2021

2020
Disentangled Generative Causal Representation Learning.
CoRR, 2020

2019
Known-class Aware Self-ensemble for Open Set Domain Adaptation.
CoRR, 2019

Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018
Domain Adaptive Semantic Segmentation Through Structure Enhancement.
Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018


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