Search Results for author: Qing Lian

Found 17 papers, 11 papers with code

The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs

1 code implementation6 Feb 2024 Tianyang Han, Qing Lian, Rui Pan, Renjie Pi, Jipeng Zhang, Shizhe Diao, Yong Lin, Tong Zhang

In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination.


MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance

1 code implementation5 Jan 2024 Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang

The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.

R-Tuning: Instructing Large Language Models to Say `I Don't Know'

1 code implementation16 Nov 2023 Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang

This approach is formalized by first identifying the disparity in knowledge encompassed by pre-trained parameters compared to that of instruction tuning data.

Hallucination Sentence

Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing

1 code implementation17 Oct 2023 Hao Lu, Yunpeng Zhang, Qing Lian, Dalong Du, Yingcong Chen

In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes.

3D Object Detection Domain Generalization +2

MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

1 code implementation18 Sep 2023 Helbert Paat, Qing Lian, Weilong Yao, Tong Zhang

In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework.

3D Object Detection object-detection

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning

no code implementations5 Sep 2023 Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan YAO, Tong Zhang

Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data.

Active Learning

Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF

no code implementations4 Sep 2023 Leheng Li, Qing Lian, Ying-Cong Chen

Deep neural networks (DNNs) have been proven extremely susceptible to adversarial examples, which raises special safety-critical concerns for DNN-based autonomous driving stacks (i. e., 3D object detection).

3D Object Detection Autonomous Driving +2

DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking

1 code implementation29 Mar 2023 Qing Lian, Tai Wang, Dahua Lin, Jiangmiao Pang

Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation.

3D Object Detection Depth Estimation +3

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

1 code implementation26 Jul 2022 Tai Wang, Qing Lian, Chenming Zhu, Xinge Zhu, Wenwei Zhang

In this technical report, we present our solution, dubbed MV-FCOS3D++, for the Camera-Only 3D Detection track in Waymo Open Dataset Challenge 2022.

object-detection Object Detection +1

MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection

1 code implementation CVPR 2022 Qing Lian, Peiliang Li, Xiaozhi Chen

Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner.

Depth Estimation Monocular 3D Object Detection +2

Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms

no code implementations2 Jun 2021 Yunqi Wang, Furui Liu, Zhitang Chen, Qing Lian, Shoubo Hu, Jianye Hao, Yik-Chung Wu

Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.

Domain Generalization

Exploring Geometric Consistency for Monocular 3D Object Detection

no code implementations CVPR 2022 Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang

In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

Autonomous Driving Data Augmentation +4

Invariant Batch Normalization for Multi-source Domain Generalization

no code implementations1 Jan 2021 Qing Lian, LIN Yong, Tong Zhang

We consider the domain generalization problem, where the test domain differs from the training domain.

Domain Generalization

Weakly Supervised Disentangled Generative Causal Representation Learning

1 code implementation6 Oct 2020 Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.


Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

1 code implementation ICCV 2019 Qing Lian, Fengmao Lv, Lixin Duan, Boqing Gong

We propose a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains.

Segmentation Semantic Segmentation +2

Known-class Aware Self-ensemble for Open Set Domain Adaptation

1 code implementation3 May 2019 Qing Lian, Wen Li, Lin Chen, Lixin Duan

Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped.

Domain Adaptation

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