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Found 10 papers, 6 papers with code
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything.
MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
RecurSeed and EdgePredictMix: Pseudo-Label Refinement Learning for Weakly Supervised Semantic Segmentation across Single- and Multi-Stage Frameworks
Although weakly supervised semantic segmentation using only image-level labels (WSSS-IL) is potentially useful, its low performance and implementation complexity still limit its application.
Ranked #10 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
Ranked #3 on
Image Manipulation Detection
on Casia V1+
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms
In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices.
Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features
The proposed network uses a stack of consecutive frames as the input and effectively learns interpolation artifacts using network blocks to learn spatiotemporal features.
Puzzle-CAM: Improved localization via matching partial and full features
Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision.
WAN: Watermarking Attack Network
Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions.
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content.
BitMix: Data Augmentation for Image Steganalysis
Convolutional neural networks (CNN) for image steganalysis demonstrate better performances with employing concepts from high-level vision tasks.
