Search Results for author:
Found 15 papers, 5 papers with code
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources.
Learning Invariant Inter-pixel Correlations for Superpixel Generation
To address this issue, we propose the Content Disentangle Superpixel (CDS) algorithm to selectively separate the invariant inter-pixel correlations and statistical properties, i. e., style noise.
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure
Experimental results demonstrate that the proposed method improves the numerical accuracy of PINNs for partial differential equations.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
Lyapunov-Stable Deep Equilibrium Models
Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models, which abandon traditional depth by solving for the fixed points of a single nonlinear layer.
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models.
Improving Neural ODEs via Knowledge Distillation
We propose a new training based on knowledge distillation to construct more powerful and robust Neural ODEs fitting image recognition tasks.
Towards Natural Robustness Against Adversarial Examples
Thus, Neural ODEs have natural robustness against adversarial examples.
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Referring Image Segmentation by Generative Adversarial Learning
To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem.
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Human parsing has received considerable interest due to its wide application potentials.
Ranked #2 on
Person Re-Identification
on Market-1501-C
A New Evaluation Protocol and Benchmarking Results for Extendable Cross-media Retrieval
Additionally, a trivial solution, \ie, directly using the predicted class label for cross-media retrieval, is tested.
Camera Fingerprint: A New Perspective for Identifying User's Identity
The underlying assumption is that multiple accounts belonging to the same person contain the same or similar camera fingerprint information.
Indexing of CNN Features for Large Scale Image Search
The convolutional neural network (CNN) features can give a good description of image content, which usually represent images with unique global vectors.
Modality-dependent Cross-media Retrieval
Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I).
