2.4k citations · 2.9k across the 16 of their papers we have counts for
17 papers
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
Xinghong Liu, Yi Zhou, Tao Zhou +2
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced s…
Adaptive Siamese Tracking with a Compact Latent Network
Xingping Dong, Jianbing Shen, Fatih Porikli +2
In this paper, we provide an intuitive viewing to simplify the Siamese-based trackers by converting the tracking task to a classification. Under this viewing, we perform an in-dept…
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Aoran Xiao, Jiaxing Huang, Dayan Guan +3
LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous det…
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
Bing Wang, Zhengdi Yu, Bo Yang +5
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields…
RGBD Object Tracking: An In-depth Review
Jinyu Yang, Zhe Li, Song Yan +4
RGBD object tracking is gaining momentum in computer vision research thanks to the development of depth sensors. Although numerous RGBD trackers have been proposed with promising p…
Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters
Mingbao Lin, Liujuan Cao, Yuxin Zhang +3
This paper focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning problem in magnitude-based…
Consistency and Diversity induced Human Motion Segmentation
Tao Zhou, Huazhu Fu, Chen Gong +4
Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster d…
Multi-Task Neural Processes
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can enco…
Specificity-Preserving Federated Learning for MR Image Reconstruction
Chun-Mei Feng, Yunlu Yan, Shanshan Wang +3
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without…
Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality
Chun-Mei Feng, Huazhu Fu, Tianfei Zhou +3
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vuln…
RGB-D Saliency Detection via Cascaded Mutual Information Minimization
Jing Zhang, Deng-Ping Fan, Yuchao Dai +4
Existing RGB-D saliency detection models do not explicitly encourage RGB and depth to achieve effective multi-modal learning. In this paper, we introduce a novel multi-stage cascad…
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Chun-Mei Feng, Yunlu Yan, Kai Yu +3
Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information,…
Specificity-preserving RGB-D Saliency Detection
Tao Zhou, Deng-Ping Fan, Geng Chen +2
Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently…
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
Bo Dong, Wenhai Wang, Deng-Ping Fan +3
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differ…
Multi-Modal Transformer for Accelerated MR Imaging
Chun-Mei Feng, Yunlu Yan, Geng Chen +3
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from it…
PVT v2: Improved Baselines with Pyramid Vision Transformer
Wenhai Wang, Enze Xie, Xiang Li +6
Transformer recently has presented encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by…
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Deng-Ping Fan, Tao Zhou, Ge-Peng Ji +5
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomo…