65 citations · 228 across the 16 of their papers we have counts for
16 papers
Debiased Learning for Remote Sensing Data
Chun-Hsiao Yeh, Xudong Wang, Stella X. Yu +4
Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e.g., ImageNet). However, r…
VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation
Xudong Wang, Ishan Misra, Ziyun Zeng +2
Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present Video…
Hierarchical Open-vocabulary Universal Image Segmentation
Xudong Wang, Shufan Li, Konstantinos Kallidromitis +3
Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions. However, complex visual scenes can be naturally decomp…
Structure-Sensitive Graph Dictionary Embedding for Graph Classification
Guangbu Liu, Tong Zhang, Xudong Wang +3
Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to tran…
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Xudong Wang, Rohit Girdhar, Stella X. Yu +1
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'disc…
Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers
Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo +2
Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping…
Debiased Learning from Naturally Imbalanced Pseudo-Labels
Xudong Wang, Zhirong Wu, Long Lian +1
Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e…
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
Xudong Wang, Long Lian, Stella X. Yu
Given an unlabeled dataset and an annotation budget, we study how to selectively label a fixed number of instances so that semi-supervised learning (SSL) on such a partially labele…
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers
Yunhui Guo, Xudong Wang, Yubei Chen +1
Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into h…
Unsupervised Visual Attention and Invariance for Reinforcement Learning
Xudong Wang, Long Lian, Stella X. Yu
Vision-based reinforcement learning (RL) is successful, but how to generalize it to unknown test environments remains challenging. Existing methods focus on training an RL policy t…
Long-tailed Recognition by Routing Diverse Distribution-Aware Experts
Xudong Wang, Long Lian, Zhongqi Miao +2
Natural data are often long-tail distributed over semantic classes. Existing recognition methods tackle this imbalanced classification by placing more emphasis on the tail data, th…
Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters
Xudong Wang, Stella X. Yu
Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels increa…
Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination
Xudong Wang, Ziwei Liu, Stella X. Yu
Unsupervised feature learning has made great strides with contrastive learning based on instance discrimination and invariant mapping, as benchmarked on curated class-balanced data…
Volumetric Attention for 3D Medical Image Segmentation and Detection
Xudong Wang, Shizhong Han, Yunqiang Chen +2
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D network…
Structure-Feature based Graph Self-adaptive Pooling
Liang Zhang, Xudong Wang, Hongsheng Li +6
Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, th…
Towards Universal Object Detection by Domain Attention
Xudong Wang, Zhaowei Cai, Dashan Gao +1
Despite increasing efforts on universal representations for visual recognition, few have addressed object detection. In this paper, we develop an effective and efficient universal…