Search Results for author:
Found 17 papers, 10 papers with code
Simple parameter-free self-attention approximation
The hybrid model of self-attention and convolution is one of the methods to lighten ViT.
2nd Place Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
Segment Any Anomaly without Training via Hybrid Prompt Regularization
We present a novel framework, i. e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models.
Ranked #1 on
Anomaly Detection
on KSDD2
A New Knowledge Distillation Network for Incremental Few-Shot Surface Defect Detection
The proposed DKAN method follows a pretraining-finetuning transfer learning paradigm and a knowledge distillation framework is designed for fine-tuning.
Informative knowledge distillation for image anomaly segmentation
However, rare attention has been paid to the overfitting problem caused by the inconsistency between the capacity of the neural network and the amount of knowledge in this scheme.
Ranked #27 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection
Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems.
Ranked #11 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
A new neighborhood structure for job shop scheduling problems
According to the above finding, this paper proposes a new N8 neighborhood structure considering the movement of critical operations within a critical block and the movement of critical operations outside the critical block.
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature Mapping
This paper proposes a novel framework, named as Pre-trained Feature Mapping (PFM), for unsupervised image anomaly detection and segmentation.
Ranked #51 on
Anomaly Detection
on MVTec AD
SDNet: mutil-branch for single image deraining using swin
The former implements the basic rain pattern feature extraction, while the latter fuses different features to further extract and process the image features.
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system.
Ranked #70 on
Anomaly Detection
on MVTec AD
Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra
Machine learning has been used to develop the model.
Differential Evolution with Better and Nearest Option for Function Optimization
Differential evolution(DE) is a conventional algorithm with fast convergence speed.
Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization
However, there are two difficulties urgently to be solved for most existing niching metaheuristic algorithms: how to set the optimal values of niching parameters for different optimization problems, and how to jump out of the local optima efficiently.
Optical Mapping Near-eye Three-dimensional Display with Correct Focus Cues
We present an optical mapping near-eye (OMNI) three-dimensional display method for wearable devices.
Whale swarm algorithm for function optimization
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization.
Simulating the joint evolution of quasars, galaxies and their large-scale distribution
The cold dark matter model has become the leading theoretical paradigm for the formation of structure in the Universe.
astro-ph
