Search Results for author:
Found 15 papers, 7 papers with code
LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation
Experimental results in classification and dense prediction tasks show that LeMeViT has a significant $1. 7 \times$ speedup, fewer parameters, and competitive performance compared to the baseline models, and achieves a better trade-off between efficiency and performance.
Data Augmentation in Human-Centric Vision
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field.
Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator
To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario.
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
To take full advantage of simulated data, we present a new unsupervised sim2real domain adaptation method for V2X collaborative detection named Decoupled Unsupervised Sim2Real Adaptation (DUSA).
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
We define perceptual gain as the increased perceptual capability when a new LiDAR is placed.
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue.
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models.
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation
Human pose estimation aims to accurately estimate a wide variety of human poses.
PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal
In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively.
Facial Attribute Transformers for Precise and Robust Makeup Transfer
In this paper, we address the problem of makeup transfer, which aims at transplanting the makeup from the reference face to the source face while preserving the identity of the source.
Roadmap on Integrated Quantum Photonics
The reduction in size, weight, power, and improvement in stability that will be enabled by QPICs will play a key role in increasing the degree of complexity and scale in quantum demonstrations.
Quantum Physics
Language-Guided Global Image Editing via Cross-Modal Cyclic Mechanism
Given the cycle, we propose several free augmentation strategies to help our model understand various editing requests given the imbalanced dataset.
Learning to Recognize the Unseen Visual Predicates
Visual relationship recognition models are limited in the ability to generalize from finite seen predicates to unseen ones.
PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer
In this paper, we address the makeup transfer task, which aims to transfer the makeup from a reference image to a source image.
UGAN: Untraceable GAN for Multi-Domain Face Translation
To address this issue, we propose a method called Untraceable GAN, which has a novel source classifier to differentiate which domain an image is translated from, and determines whether the translated image still retains the characteristics of the source domain.
