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20142023
most citedUnsupervised Feature Learning via Non-Parametric Instance-level Discrimination

174 citations · 711 across the 57 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

eess.IV2022★ 2 cited

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…

cs.CV2022

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…

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

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…

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…

cs.CV2022

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…

cs.LG2022★ 13 cited

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…