1 code implementation • 23 Dec 2021 • Xi Ouyang, Srikrishna Karanam, Ziyan Wu, Terrence Chen, Jiayu Huo, Xiang Sean Zhou, Qian Wang, Jie-Zhi Cheng
However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications.
no code implementations • 27 Jul 2021 • Runze Li, Srikrishna Karanam, Ren Li, Terrence Chen, Bir Bhanu, Ziyan Wu
We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.
no code implementations • ICCV 2021 • Abhishek Aich, Meng Zheng, Srikrishna Karanam, Terrence Chen, Amit K. Roy-Chowdhury, Ziyan Wu
To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.
Ranked #2 on
Person Re-Identification
on DukeMTMC-VideoReID
no code implementations • 13 Jul 2021 • Ren Li, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu
Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.
Ranked #1 on
3D Human Shape Estimation
on SSP-3D
(PVE-T metric)
no code implementations • CVPR 2021 • Yunhao Ge, Yao Xiao, Zhi Xu, Meng Zheng, Srikrishna Karanam, Terrence Chen, Laurent Itti, Ziyan Wu
Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability.
no code implementations • ICCV 2021 • Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje
Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.
no code implementations • 13 Aug 2020 • Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu
We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.
no code implementations • ECCV 2020 • Georgios Georgakis, Ren Li, Srikrishna Karanam, Terrence Chen, Jana Kosecka, Ziyan Wu
In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.
2 code implementations • CVPR 2020 • Wenqian Liu, Runze Li, Meng Zheng, Srikrishna Karanam, Ziyan Wu, Bir Bhanu, Richard J. Radke, Octavia Camps
We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions.
no code implementations • 18 Nov 2019 • Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i. e., explaining why the input set of images is similar or dissimilar.
no code implementations • 18 Nov 2019 • Ren Li, Changjiang Cai, Georgios Georgakis, Srikrishna Karanam, Terrence Chen, Ziyan Wu
We consider the problem of human pose estimation.
no code implementations • NeurIPS 2019 • Benjamin Planche, Xuejian Rong, Ziyan Wu, Srikrishna Karanam, Harald Kosch, YingLi Tian, Jan Ernst, Andreas Hutter
We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior, (b) real observations of a scene can be incorporated while observing global consistency, (c) unobserved regions can be hallucinated locally in consistence with previous observations, hallucinations and global priors, and (d) hallucinations are statistical in nature, i. e., different scenes can be generated from the same observations.
1 code implementation • ICCV 2019 • Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu, Dimitris N. Metaxas
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.
no code implementations • CVPR 2019 • Meng Zheng, Srikrishna Karanam, Ziyan Wu, Richard J. Radke
We propose a new deep architecture for person re-identification (re-id).
1 code implementation • ICCV 2019 • Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, Jana Kosecka
In this paper, we solve this key problem of existing methods requiring expensive 3D pose annotations by proposing a new method that matches RGB images to CAD models for object pose estimation.
no code implementations • 16 Aug 2018 • Meng Zheng, Srikrishna Karanam, Richard J. Radke
Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research.
no code implementations • CVPR 2018 • Georgios Georgakis, Srikrishna Karanam, Ziyan Wu, Jan Ernst, Jana Kosecka
Finding correspondences between images or 3D scans is at the heart of many computer vision and image retrieval applications and is often enabled by matching local keypoint descriptors.
no code implementations • CVPR 2018 • Yunye Gong, Srikrishna Karanam, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, Peter C. Doerschuk
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data.
no code implementations • 2 Jun 2017 • Srikrishna Karanam, Eric Lam, Richard J. Radke
Designing useful person re-identification systems for real-world applications requires attention to operational aspects not typically considered in academic research.
3 code implementations • 31 May 2016 • Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke
To ensure a fair comparison, all of the approaches were implemented using a unified code library that includes 11 feature extraction algorithms and 22 metric learning and ranking techniques.
no code implementations • ICCV 2015 • Srikrishna Karanam, Yang Li, Richard J. Radke
This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view.