19 citations · 27 across the 3 of their papers we have counts for
8 papers
Sequential Ensembling for Semantic Segmentation
Rawal Khirodkar, Brandon Smith, Siddhartha Chandra +2
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications. In…
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Rawal Khirodkar, Visesh Chari, Amit Agrawal +1
A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failure…
PoseNet3D: Learning Temporally Consistent 3D Human Pose via Knowledge Distillation
Shashank Tripathi, Siddhant Ranade, Ambrish Tyagi +1
Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose a novel neural network framework, PoseNet3D, that takes 2D joints as input and outputs 3D skel…
Unsupervised 3D Pose Estimation with Geometric Self-Supervision
Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal +4
We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data,…
Learning to Generate Synthetic Data via Compositing
Shashank Tripathi, Siddhartha Chandra, Amit Agrawal +3
We present a task-aware approach to synthetic data generation. Our framework employs a trainable synthesizer network that is optimized to produce meaningful training samples by ass…
Can 3D Pose be Learned from 2D Projections Alone?
Dylan Drover, Rohith MV, Ching-Hang Chen +3
3D pose estimation from a single image is a challenging task in computer vision. We present a weakly supervised approach to estimate 3D pose points, given only 2D pose landmarks. O…