Search Results for author:
Found 7 papers, 1 papers with code
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content.
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion
To our best knowledge, this is the first work to offer an efficient second-order approximation in solving inverse problems using latent diffusion and editing real-world images with corruptions.
JIANG: Chinese Open Foundation Language Model
With the advancements in large language model technology, it has showcased capabilities that come close to those of human beings across various tasks.
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference
In this work, a sparsity-driven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated.
Rare geometries: revealing rare categories via dimension-driven statistics
The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features).
Adversarial Occlusion-aware Face Detection
This paper introduces an Adversarial Occlusion-aware Face Detector (AOFD) by simultaneously detecting occluded faces and segmenting occluded areas.
Ranked #2 on
Occluded Face Detection
on MAFA
GM-Net: Learning Features with More Efficiency
Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images.
