Pinned Repositories
ClustSeg
CSrankings
A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.
dplab2
HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
HW3_object_track
HW4_trajectory
Instance_Unique_Querying
[NeurIPS 2022 Spotlight] Learning Equivariant Segmentation with Instance-Unique Querying
jamesliang819.github.io
manydepth
[CVPR 2021] Self-supervised depth estimation from short sequences
mmcv
OpenMMLab Computer Vision Foundation
JamesLiang819's Repositories
JamesLiang819/Instance_Unique_Querying
[NeurIPS 2022 Spotlight] Learning Equivariant Segmentation with Instance-Unique Querying
JamesLiang819/ClustSeg
JamesLiang819/HW3_object_track
JamesLiang819/HW4_trajectory
JamesLiang819/mmcv
OpenMMLab Computer Vision Foundation
JamesLiang819/CSrankings
A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.
JamesLiang819/dplab2
JamesLiang819/HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
JamesLiang819/jamesliang819.github.io
JamesLiang819/manydepth
[CVPR 2021] Self-supervised depth estimation from short sequences
JamesLiang819/mmdetection
OpenMMLab Detection Toolbox and Benchmark
JamesLiang819/testmmdet
JamesLiang819/UniPose
We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.