Search Results for author:
Found 18 papers, 8 papers with code
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer
Emotion recognition aims to discern the emotional state of subjects within an image, relying on subject-centric and contextual visual cues.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM).
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
CageViT: Convolutional Activation Guided Efficient Vision Transformer
Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input sequence.
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors such as commercial competition and sample collection difficulties.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
Deep Industrial Image Anomaly Detection: A Survey
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Cameras
This paper proposes a novel application system for the generation of three-dimensional (3D) character animation driven by markerless human body motion capturing.
Towards Continual Adaptation in Industrial Anomaly Detection
We believe that the proposed task and benchmark will be beneficial to the field of AD.
A Survey of Visual Sensory Anomaly Detection
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
Cross-Modality Neuroimage Synthesis: A Survey
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation
Annotation burden has become one of the biggest barriers to semantic segmentation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search
Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.
