Search Results for author:
Found 16 papers, 7 papers with code
MRI Reconstruction via Cascaded Channel-wise Attention Network
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.
Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
In this paper, we propose a new box-based cell instance segmentation method.
ASSD: Attentive Single Shot Multibox Detector
This paper proposes a new deep neural network for object detection.
Object-Guided Instance Segmentation for Biological Images
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
Point Cloud Processing via Recurrent Set Encoding
The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation.
Vertebra-Focused Landmark Detection for Scoliosis Assessment
The comparison results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i. e., only a small portion of nuclei locations in each image are labeled.
Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.
Ranked #11 on
Oriented Object Detection
on DOTA 1.0
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.
Enhanced MRI Reconstruction Network using Neural Architecture Search
The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework.
AVT: Audio-Video Transformer for Multimodal Action Recognition
AVT uses a combination of video and audio signals to improve action recognition accuracy, leveraging the effective spatio-temporal representation by the video Transformer.
Ranked #4 on
Multi-modal Classification
on VGG-Sound
Multiscale Multimodal Transformer for Multimodal Action Recognition
In this work, we develop a multiscale multimodal Transformer (MMT) that employs hierarchical representation learning.
Ranked #1 on
Multi-modal Classification
on VGG-Sound
Dynamic Inference With Grounding Based Vision and Language Models
For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks.
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models
While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks.
