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20182022
most citedSharp Multiple Instance Learning for DeepFake Video Detection

154 citations · 340 across the 16 of their papers we have counts for

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16 papers · 1 filter

cs.CV202274 cited

Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective

Yao Zhu, Yuefeng Chen, Xiaodan Li +6

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…

cs.CV2021

Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning

Jianfeng Dong, Zhe Ma, Xiaofeng Mao +4

This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute…

cs.CV20213 cited

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

Xiaodan Li, Jinfeng Li, Yuefeng Chen +5

We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query acce…

cs.CV2021

Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association

Peisong Wen, Qianqian Xu, Yangbangyan Jiang +3

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community…

cs.CV202123 cited

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

Honggu Liu, Xiaodan Li, Wenbo Zhou +5

The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step o…

cs.CV20211 cited

Hierarchical Similarity Learning for Language-based Product Image Retrieval

Zhe Ma, Fenghao Liu, Jianfeng Dong +3

This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measur…