activity
20182022
most citedCombining Label Propagation and Simple Models Out-performs Graph Neural Networks

114 citations · 312 across the 19 of their papers we have counts for

collaborators

33 papers

cs.CV2022

Totems: Physical Objects for Verifying Visual Integrity

Jingwei Ma, Lucy Chai, Minyoung Huh +4

We introduce a new approach to image forensics: placing physical refractive objects, which we call totems, into a scene so as to protect any photograph taken of that scene. Totems…

cs.CV20229 cited

ObjectFormer for Image Manipulation Detection and Localization

Junke Wang, Zuxuan Wu, Jingjing Chen +4

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this…

cs.LG202159 cited

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Derek Lim, Felix Hohne, Xiuyu Li +4

Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networ…

cs.CV20212 cited

A Frequency Perspective of Adversarial Robustness

Shishira R Maiya, Max Ehrlich, Vatsal Agarwal +3

Adversarial examples pose a unique challenge for deep learning systems. Despite recent advances in both attacks and defenses, there is still a lack of clarity and consensus in the…

cs.CV20213 cited

NeRV: Neural Representations for Videos

Hao Chen, Bo He, Hanyu Wang +3

We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we rep…

cs.CV202116 cited

MixNorm: Test-Time Adaptation Through Online Normalization Estimation

Xuefeng Hu, Gokhan Uzunbas, Sirius Chen +4

We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples. Known as Test-Time Adaptation, mo…