activity
20152023
most citedResNet or DenseNet? Introducing Dense Shortcuts to ResNet

18 citations · 26 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition

Xinyu Gong, Sreyas Mohan, Naina Dhingra +4

In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG). MMG aims to study how systems can generalize when data…

cs.CV2022★ 5 cited

SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields

Siddhant Ranade, Christoph Lassner, Kai Li +4

Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized by the scene's plenoptic function. This is achieved by using an MLP together with a mapping to a higher-…

cs.CV2022★ 2 cited

A Deep Perceptual Measure for Lens and Camera Calibration

Yannick Hold-Geoffroy, Dominique Piché-Meunier, Kalyan Sunkavalli +3

Image editing and compositing have become ubiquitous in entertainment, from digital art to AR and VR experiences. To produce beautiful composites, the camera needs to be geometrica…

cs.CV2021

Sampling Based Scene-Space Video Processing

Felix Klose, Oliver Wang, Jean-Charles Bazin +2

Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly depende…

cs.CV2020★ 18 cited

ResNet or DenseNet? Introducing Dense Shortcuts to ResNet

Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5

ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…

cs.CV2015

Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

Tae-Hyun Oh, Yu-Wing Tai, Jean-Charles Bazin +2

Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers.…