Search Results for author:
Found 8 papers, 3 papers with code
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Dual-path Frequency Discriminators for Few-shot Anomaly Detection
We consider anomaly detection as a discriminative classification problem, wherefore the dual-path feature discrimination module is employed to detect and locate the image-level and feature-level anomalies in the feature space.
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects.
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
In this paper, we propose a data-free mixed-precision compensation (DF-MPC) method to recover the performance of an ultra-low precision quantized model without any data and fine-tuning process.
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation
Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks.
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case.
Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks
We propose an end to end deep learning approach for generating real-time facial animation from just audio.
