Search Results for author:
Found 8 papers, 3 papers with code
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs.
V2C: Visual Voice Cloning
Existing Voice Cloning (VC) tasks aim to convert a paragraph text to a speech with desired voice specified by a reference audio.
Rapid Elastic Architecture Search under Specialized Classes and Resource Constraints
More critically, EAS is able to find compact architectures within 0. 1 second for 50 deployment scenarios.
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds.
Ranked #9 on
Semantic Segmentation
on KITTI-360
DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency
DualPoseNet stacks two parallel pose decoders on top of a shared pose encoder, where the implicit decoder predicts object poses with a working mechanism different from that of the explicit one; they thus impose complementary supervision on the training of pose encoder.
Ranked #4 on
6D Pose Estimation using RGBD
on REAL275
FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
An Iterative Boundary Random Walks Algorithm for Interactive Image Segmentation
Two approaches, iterative random walks and boundary random walks, are proposed for segmentation potential, which is the key step in feedback system.
