Search Results for author:
Found 13 papers, 8 papers with code
Free-Editor: Zero-shot Text-driven 3D Scene Editing
To date, editing 3D scenes requires either re-training the model to adapt to various 3D edited scenes or design-specific methods for each special editing type.
LatentEditor: Text Driven Local Editing of 3D Scenes
Our approach achieves faster editing speeds and superior output quality compared to existing 3D editing models, bridging the gap between textual instructions and high-quality 3D scene editing in latent space.
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction
Machine learning plays a vital role in industrial HRI by enhancing the adaptability and autonomy of robots in complex environments.
Efficient Backdoor Removal Through Natural Gradient Fine-tuning
Extensive experiments show that the proposed method achieves state-of-the-art performance on a wide range of backdoor defense benchmarks: four different datasets- CIFAR10, GTSRB, Tiny-ImageNet, and ImageNet; 13 recent backdoor attacks, e. g.
Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model
It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling.
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts.
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning
Recently, the use of small pre-trained models has been shown effective in federated learning optimization and improving convergence.
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples.
RF Signal Transformation and Classification using Deep Neural Networks
Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets.
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
The method proposed in this work referred to as RODD outperforms SOTA detection performance on an extensive suite of benchmark datasets on OOD detection tasks.
Ranked #1 on
Out-of-Distribution Detection
on cifar100
(using extra training data)
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning
Through comparing adversarial robustness achieved without adversarial training, with triplet loss adversarial training, and our contrastive pre-training combined with triplet loss adversarial fine-tuning, we find that our method achieves comparable results with far fewer epochs re-quired during fine-tuning.
Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection
The problem of simultaneous column and row subset selection is addressed in this paper.
