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Found 9 papers, 4 papers with code
Double Privacy Guard: Robust Traceable Adversarial Watermarking against Face Recognition
This strategy enhances the representation of universal carrier features, mitigating multi-objective optimization conflicts in watermarking.
Unsupervised Template-assisted Point Cloud Shape Correspondence Network
Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds.
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer.
AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
With the continuous improvements of deepfake methods, forgery messages have transitioned from single-modality to multi-modal fusion, posing new challenges for existing forgery detection algorithms.
Dual Defense: Adversarial, Traceable, and Invisible Robust Watermarking against Face Swapping
Dual Defense invisibly embeds a single robust watermark within the target face to actively respond to sudden cases of malicious face swapping.
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #1 on
Unsupervised Monocular Depth Estimation
on KITTI-C
(using extra training data)
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition
In this work, we propose a novel and effective defense mechanism termed the Universal Defensive Underpainting Patch (UDUP) that modifies the underpainting of text images instead of the characters.
Optical Character Recognition
Optical Character Recognition (OCR)
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.
Ranked #2 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Query Refinement Transformer for 3D Instance Segmentation
Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.
Ranked #3 on
3D Instance Segmentation
on ScanNet(v2)
