activity
20172022
most citedTopology-Preserving Deep Image Segmentation

85 citations · 230 across the 22 of their papers we have counts for

collaborators
Showing cs.CVShow all

26 papers · 1 filter

cs.CV20223 cited

Patch-level Gaze Distribution Prediction for Gaze Following

Qiaomu Miao, Minh Hoai, Dimitris Samaras

Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located outside the image. Recent works det…

cs.CV2021

SIDER: Single-Image Neural Optimization for Facial Geometric Detail Recovery

Aggelina Chatziagapi, ShahRukh Athar, Francesc Moreno-Noguer +1

We present SIDER(Single-Image neural optimization for facial geometric DEtail Recovery), a novel photometric optimization method that recovers detailed facial geometry from a singl…

cs.CV20215 cited

FLAME-in-NeRF : Neural control of Radiance Fields for Free View Face Animation

ShahRukh Athar, Zhixin Shu, Dimitris Samaras

This paper presents a neural rendering method for controllable portrait video synthesis. Recent advances in volumetric neural rendering, such as neural radiance fields (NeRF), has…

cs.CV20212 cited

Temporal Feature Warping for Video Shadow Detection

Shilin Hu, Hieu Le, Dimitris Samaras

While single image shadow detection has been improving rapidly in recent years, video shadow detection remains a challenging task due to data scarcity and the difficulty in modelli…

cs.CV202114 cited

Topology-Aware Segmentation Using Discrete Morse Theory

Xiaoling Hu, Yusu Wang, Li Fuxin +2

In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel conne…

cs.CV2021

Hierarchical Proxy-based Loss for Deep Metric Learning

Zhibo Yang, Muhammet Bastan, Xinliang Zhu +2

Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. However, existing proxy-based losses focus on learni…