no code implementations • ECCV 2020 • Viveka Kulharia, Siddhartha Chandra, Amit Agrawal, Philip Torr, Ambrish Tyagi
We propose a weakly supervised approach to semantic segmentation using bounding box annotations.
no code implementations • CVPR 2023 • Vinoj Jayasundara, Amit Agrawal, Nicolas Heron, Abhinav Shrivastava, Larry S. Davis
We present FlexNeRF, a method for photorealistic freeviewpoint rendering of humans in motion from monocular videos.
no code implementations • 8 Oct 2022 • Rawal Khirodkar, Brandon Smith, Siddhartha Chandra, Amit Agrawal, Antonio Criminisi
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications.
Ranked #10 on Semantic Segmentation on PASCAL Context
1 code implementation • ICCV 2021 • Rawal Khirodkar, Visesh Chari, Amit Agrawal, Ambrish Tyagi
Specifically, we achieve 70. 0 AP on CrowdPose and 42. 5 AP on OCHuman test sets, a significant improvement of 2. 4 AP and 6. 5 AP over the prior art, respectively.
Ranked #1 on Multi-Person Pose Estimation on OCHuman
1 code implementation • 7 Mar 2020 • Shashank Tripathi, Siddhant Ranade, Ambrish Tyagi, Amit Agrawal
Finally, both the teacher and the student networks are jointly fine-tuned in an end-to-end manner using temporal, self-consistency and adversarial losses, improving the accuracy of each individual network.
Ranked #70 on 3D Human Pose Estimation on MPI-INF-3DHP (using extra training data)
no code implementations • CVPR 2019 • Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James M. Rehg, Visesh Chari
The synthesizer and target networks are trained in an adversarial manner wherein each network is updated with a goal to outdo the other.
no code implementations • CVPR 2019 • Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal, Dylan Drover, Rohith MV, Stefan Stojanov, James M. Rehg
Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data.
Ranked #73 on 3D Human Pose Estimation on MPI-INF-3DHP (AUC metric)
no code implementations • 16 Dec 2018 • Bhaskar Gautam, Annappa Basava, Abhishek Singh, Amit Agrawal
The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized.
no code implementations • 22 Aug 2018 • Dylan Drover, Rohith MV, Ching-Hang Chen, Amit Agrawal, Ambrish Tyagi, Cong Phuoc Huynh
We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks.
no code implementations • 29 Apr 2018 • Cong Phuoc Huynh, Arridhana Ciptadi, Ambrish Tyagi, Amit Agrawal
Such a conditional generative model can produce multiple novel samples of complementary items (in the feature space) for a given query item.
12 code implementations • CVPR 2018 • Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal
In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps.
Ranked #7 on Semantic Segmentation on PASCAL VOC 2012 test
no code implementations • CVPR 2013 • Amit Agrawal, Srikumar Ramalingam
We describe such setups as multi-axial imaging systems, since a single sphere results in an axial system.