activity
20162022
most citedLarge-Scale Long-Tailed Recognition in an Open World

65 citations · 116 across the 14 of their papers we have counts for

collaborators

31 papers

cs.CV2022

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…

cs.CV2022

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…

eess.IV20211 cited

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…

cs.SD20212 cited

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…

cs.CV2021

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…

cs.CV2021

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…