Search Results for author: Mingyan Zhu

Found 5 papers, 2 papers with code

Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger

no code implementations3 Dec 2023 Yiming Li, Mingyan Zhu, Junfeng Guo, Tao Wei, Shu-Tao Xia, Zhan Qin

We argue that the intensity constraint of existing SSBAs is mostly because their trigger patterns are `content-irrelevant' and therefore act as `noises' for both humans and DNNs.

Attribute Backdoor Attack

One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

1 code implementation5 Aug 2023 Hang Guo, Tao Dai, Mingyan Zhu, Guanghao Meng, Bin Chen, Zhi Wang, Shu-Tao Xia

Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition.

Contrastive Learning Knowledge Distillation +2

High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction

no code implementations3 Jan 2023 Boyu Zhang, Hongliang Yuan, Mingyan Zhu, Ligang Liu, Jue Wang

Generating high-quality, realistic rendering images for real-time applications generally requires tracing a few samples-per-pixel (spp) and using deep learning-based approaches to denoise the resulting low-spp images.

2k Denoising

Controller-Guided Partial Label Consistency Regularization with Unlabeled Data

no code implementations20 Oct 2022 Qian-Wei Wang, Bowen Zhao, Mingyan Zhu, Tianxiang Li, Zimo Liu, Shu-Tao Xia

Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid.

Contrastive Learning Data Augmentation +2

Black-box Dataset Ownership Verification via Backdoor Watermarking

1 code implementation4 Aug 2022 Yiming Li, Mingyan Zhu, Xue Yang, Yong Jiang, Tao Wei, Shu-Tao Xia

The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e. g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods.

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