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Found 10 papers, 4 papers with code
Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Ranked #1 on
Image-Based Localization
on cvact
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
Image Demoireing with Learnable Bandpass Filters
Image demoireing is a multi-faceted image restoration task involving both texture and color restoration.
Ranked #2 on
Image Enhancement
on TIP 2018
(using extra training data)
AIM 2019 Challenge on Image Demoireing: Methods and Results
A new dataset, called LCDMoire was created for this challenge, and consists of 10, 200 synthetically generated image pairs (moire and clean ground truth).
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
A dense block was introduced to improve the performance of extractor in DRU.
Color Image Compression Artifact Reduction
Image Compression Artifact Reduction
+3
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction.
Ranked #4 on
JPEG Artifact Correction
on LIVE1 (Quality 20 Color)
JPEG Artifact Correction
Jpeg Compression Artifact Reduction
