Search Results for author:
Found 8 papers, 3 papers with code
A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation
Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts.
LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers
However, generalizing these methods to unseen speakers incurs catastrophic performance degradation due to the limited number of speakers in training bank and the evident visual variations caused by the shape/color of lips for different speakers.
Asymmetric Cross-Scale Alignment for Text-Based Person Search
To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment.
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up
Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task.
Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense
Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.
HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing
We develop a novel \textit{hybrid parallelism} method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center.
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.
