174 citations · 711 across the 57 of their papers we have counts for
60 papers
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
Iksung Kang, Qinrong Zhang, Stella X. Yu +1
Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by…
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual Grouping
Long Lian, Zhirong Wu, Stella X. Yu
We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move…
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…
High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting
Ke Wang, Mariya Doneva, Jakob Meineke +6
Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single…
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy
Long Lian, Zhirong Wu, Stella X. Yu
We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. Previous methods in unsupervised video object segmentation…
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
Utkarsh Singhal, Stella X. Yu, Zackery Steck +2
Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired…
Learning Hierarchical Image Segmentation For Recognition and By Recognition
Tsung-Wei Ke, Sangwoo Mo, Stella X. Yu
Large vision and language models learned directly through image-text associations often lack detailed visual substantiation, whereas image segmentation tasks are treated separately…
Unsupervised Scene Sketch to Photo Synthesis
Jiayun Wang, Sangryul Jeon, Stella X. Yu +3
Sketches make an intuitive and powerful visual expression as they are fast executed freehand drawings. We present a method for synthesizing realistic photos from scene sketches. Wi…
Open Long-Tailed Recognition in a Dynamic World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan +3
Real world data often exhibits a long-tailed and open-ended (with unseen classes) distribution. A practical recognition system must balance between majority (head) and minority (ta…
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…
Co-domain Symmetry for Complex-Valued Deep Learning
Utkarsh Singhal, Yifei Xing, Stella X. Yu
We study complex-valued scaling as a type of symmetry natural and unique to complex-valued measurements and representations. Deep Complex Networks (DCN) extends real-valued algebra…
The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos
Runtao Liu, Zhirong Wu, Stella X. Yu +1
Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement…
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
Ke Wang, Jonathan I Tamir, Alfredo De Goyeneche +4
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFL…
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…
Compact and Optimal Deep Learning with Recurrent Parameter Generators
Jiayun Wang, Yubei Chen, Stella X. Yu +2
Deep learning has achieved tremendous success by training increasingly large models, which are then compressed for practical deployment. We propose a drastically different approach…
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu +2
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transf…
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
Tsung-Wei Ke, Jyh-Jing Hwang, Stella X. Yu
Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, label…
Iterative Human and Automated Identification of Wildlife Images
Zhongqi Miao, Ziwei Liu, Kaitlyn M. Gaynor +3
Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced autom…
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…
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang, Michael Kellman, Christopher M. Sandino +5
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can…
Unsupervised deep learning for grading of age-related macular degeneration using retinal fundus images
Baladitya Yellapragada, Sascha Hornhauer, Kiersten Snyder +2
Many diseases are classified based on human-defined rubrics that are prone to bias. Supervised neural networks can automate the grading of retinal fundus images, but require labor-…
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…