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Found 10 papers, 0 papers with code
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection
To fully exploit saliency guidance, on each map, we select a pixel pair from the cluster with the highest centroid saliency to form a patch pair.
Image-Pointcloud Fusion based Anomaly Detection using PD-REAL Dataset
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain.
Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction
Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction.
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization
Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons.
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples
Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism.
Learning Disentangled Representations for Controllable Human Motion Prediction
Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion.
Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder
In this paper, we present a deep generative model based method to generate diverse human motion interpolation results.
Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks
Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input.
Example-based Color Transfer with Gaussian Mixture Modeling
In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids.
Blur Removal via Blurred-Noisy Image Pair
Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images.
