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Jianmin Li

15 accepted papers

2024

Full-Distance Evasion of Pedestrian Detectors in the Physical World

NeurIPS 2024poster

Many studies have proposed attack methods to generate adversarial patterns for evading pedestrian detection, alarming the computer vision community about the need for more attention to the robustness of detectors. However, adversarial patterns optimized by these methods commonly have limited perform…

2024

InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image

ICLR 2024poster

With the success of Neural Radiance Field (NeRF) in 3D-aware portrait editing, a variety of works have achieved promising results regarding both quality and 3D consistency. However, these methods heavily rely on per-prompt optimization when handling natural language as editing instructions. Due to t…

2024

Learning Natural Consistency Representation for Face Forgery Video Detection

ECCV 2024poster

"Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to specific forgery methods or videos, and existing self-supervised detectors are strict on auxiliary tasks, such as requiri…

2024

SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

CVPR 2024poster

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However the computational costs introduced by the dense feature maps grow quadratically as the per…

2023

HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

NeurIPS 2023poster

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels…

2023

PREIM3D: 3D Consistent Precise Image Attribute Editing From a Single Image

CVPR 2023poster

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent…

2022

AutoLoss-GMS: Searching Generalized Margin-Based Softmax Loss Function for Person Re-Identification

CVPR 2022poster

Person re-identification is a hot topic in computer vision, and the loss function plays a vital role in improving the discrimination of the learned features. However, most existing models utilize the hand-crafted loss functions, which are usually sub-optimal and challenging to be designed. In this p…

Cited by 35PDFScholar
2022

Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World

CVPR 2022oral

Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated t…

Cited by 73PDFScholar
2021

Attack on Practical Speaker Verification System Using Universal Adversarial Perturbations

ICASSP 2021accepted

In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal to perform physical attacks, which would be easily rejected by audio replay det…

Cited by 0SourceScholar
2021

Fooling Thermal Infrared Pedestrian Detectors in Real World Using Small Bulbs

AAAI 2021technical

Thermal infrared detection systems play an important role in many areas such as night security, autonomous driving, and body temperature detection. They have the unique advantages of passive imaging, temperature sensitivity and penetration. But the security of these systems themselves has not been f…

Cited by 95SourcePDFScholar
2021

Look Closer To Segment Better: Boundary Patch Refinement for Instance Segmentation

CVPR 2021poster

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary p…

Cited by 114PDFcodeScholar
2021

RefineMask: Towards High-Quality Instance Segmentation With Fine-Grained Features

CVPR 2021poster

The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In th…

Cited by 154PDFcodeScholar