Search Results for author: Anurag Ranjan

Found 16 papers, 11 papers with code

An Improved One millisecond Mobile Backbone

no code implementations8 Jun 2022 Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan

Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device.

Image Classification object-detection +2

NeuMan: Neural Human Radiance Field from a Single Video

no code implementations23 Mar 2022 Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, Anurag Ranjan

Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences.

Token Pooling in Vision Transformers

no code implementations8 Oct 2021 Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, Oncel Tuzel

Token Pooling is a simple and effective operator that can benefit many architectures.

LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time

1 code implementation8 Oct 2021 Elvis Nunez, Maxwell Horton, Anish Prabhu, Anurag Ranjan, Ali Farhadi, Mohammad Rastegari

Our models require no retraining, thus our subspace of models can be deployed entirely on-device to allow adaptive network compression at inference time.

Quantization

MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias

no code implementations9 Dec 2020 Nataniel Ruiz, Barry-John Theobald, Anurag Ranjan, Ahmed Hussein Abdelaziz, Nicholas Apostoloff

Images generated using MorphGAN conserve the identity of the person in the original image, and the provided control over head pose and facial expression allows test sets to be created to identify robustness issues of a facial recognition deep network with respect to pose and expression.

Data Augmentation Face Generation +1

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

1 code implementation ICCV 2021 Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, Joshua M. Susskind

To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.

Multi-Task Learning Natural Language Processing +2

Learning Multi-Human Optical Flow

2 code implementations24 Oct 2019 Anurag Ranjan, David T. Hoffmann, Dimitrios Tzionas, Siyu Tang, Javier Romero, Michael J. Black

Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset.

Optical Flow Estimation

Attacking Optical Flow

1 code implementation ICCV 2019 Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black

In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance.

Optical Flow Estimation Self-Driving Cars

Capture, Learning, and Synthesis of 3D Speaking Styles

1 code implementation CVPR 2019 Daniel Cudeiro, Timo Bolkart, Cassidy Laidlaw, Anurag Ranjan, Michael J. Black

To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers.

3D Face Animation Talking Face Generation

Generating 3D faces using Convolutional Mesh Autoencoders

2 code implementations ECCV 2018 Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black

To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.

3D FACE MODELING Face Model

Learning Human Optical Flow

1 code implementation14 Jun 2018 Anurag Ranjan, Javier Romero, Michael J. Black

Given this, we devise an optical flow algorithm specifically for human motion and show that it is superior to generic flow methods.

Optical Flow Estimation

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

1 code implementation CVPR 2019 Anurag Ranjan, Varun Jampani, Lukas Balles, Kihwan Kim, Deqing Sun, Jonas Wulff, Michael J. Black

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.

Monocular Depth Estimation Motion Estimation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.