Search Results for author:
Found 12 papers, 5 papers with code
HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection
Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively.
Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection
The cross-dataset search is also considered to develop more general architectures.
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification.
Ranked #4 on
Anomaly Detection
on MVTec LOCO AD
TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization
In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization.
Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
We present a super-resolution model for an advection-diffusion process with limited information.
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
CAFE: Catastrophic Data Leakage in Federated Learning
In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.
VAFL: a Method of Vertical Asynchronous Federated Learning
Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients.
Knowledge Distillation via Route Constrained Optimization
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
Correlation Congruence for Knowledge Distillation
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
Transductive Centroid Projection for Semi-supervised Large-scale Recognition
It is inspired by the observation of the weights in classification layer (called extit{anchors}) converge to the central direction of each class in hyperspace.
