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20202022
most citedDiscriminator-Free Generative Adversarial Attack

21 citations · 25 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV20233 cited

Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning

Qiang Wang, Junlong Du, Ke Yan +1

The Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training and has applied to many downstream tasks. We explore the ada…

cs.CV2023

HODN: Disentangling Human-Object Feature for HOI Detection

Shuman Fang, Zhiwen Lin, Ke Yan +3

The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances cur…

cs.CV20224 cited

Expanding Low-Density Latent Regions for Open-Set Object Detection

Jiaming Han, Yuqiang Ren, Jian Ding +3

Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categori…

cs.CV2021

Transformer-based Dual Relation Graph for Multi-label Image Recognition

Jiawei Zhao, Ke Yan, Yifan Zhao +3

The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsist…

cs.CV2021

Heterogeneous Relational Complement for Vehicle Re-identification

Jiajian Zhao, Yifan Zhao, Jia Li +2

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning vi…

cs.CV202121 cited

Discriminator-Free Generative Adversarial Attack

Shaohao Lu, Yuqiao Xian, Ke Yan +5

The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images. Most of t…