activity
20192022
most citedD^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention

15 citations · 37 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV20222 cited

Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection

Linfeng Zhang, Yukang Shi, Hung-Shuo Tai +4

Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision. Recently, significant breakthrough has been made in multi-view 3D detection tasks. Howev…

cs.CV2022

Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

Yunqing Hu, Xuan Jin, Yin Zhang +5

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base…

cs.CV202215 cited

D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention

Junyu Lin, Xiaofeng Mao, Yuefeng Chen +3

DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing. However, it suffers from problems such as low performance and slow con…

cs.CV20216 cited

Unrestricted Adversarial Attacks on ImageNet Competition

Yuefeng Chen, Xiaofeng Mao, Yuan He +34

Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…

cs.CV20211 cited

Adversarial Attacks on ML Defense Models Competition

Yinpeng Dong, Qi-An Fu, Xiao Yang +25

Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…

cs.CV202111 cited

AdvDrop: Adversarial Attack to DNNs by Dropping Information

Ranjie Duan, Yuefeng Chen, Dantong Niu +3

Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e.g. cartoon. However, in terms of visual perception of Deep N…