Search Results for author: Shubham Jain

Found 13 papers, 4 papers with code

ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning

no code implementations11 Oct 2022 Nicholas Meegan, Hansi Liu, Bryan Cao, Abrar Alali, Kristin Dana, Marco Gruteser, Shubham Jain, Ashwin Ashok

We introduce ViFiCon, a self-supervised contrastive learning scheme which uses synchronized information across vision and wireless modalities to perform cross-modal association.

Contrastive Learning Region Proposal

Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors

1 code implementation ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) 2022 Hansi Liu, Abrar Alali, Mohamed Ibrahim, Bryan Bo Cao, Nicholas Meegan, Hongyu Li, Marco Gruteser, Shubham Jain, Kristin Dana, Ashwin Ashok, Bin Cheng, HongSheng Lu

In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e. g. WiFi MAC address).

Graph Matching Multimodal Association

RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classification

1 code implementation23 Feb 2022 Moinak Bhattacharya, Shubham Jain, Prateek Prasanna

RadioTransformer fills this critical gap by learning from radiologists' visual search patterns, encoded as 'human visual attention regions' in a cascaded global-focal transformer framework.

Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework

no code implementations19 Jan 2022 Junpeng Wang, Liang Wang, Yan Zheng, Chin-Chia Michael Yeh, Shubham Jain, Wei zhang

With these metrics, one can easily identify meta-features with the most complementary behaviors in two classifiers, and use them to better ensemble the classifiers.

Binary Classification

Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration

no code implementations25 Nov 2020 Reena Elangovan, Shubham Jain, Anand Raghunathan

To efficiently support precision re-configurability in DNN accelerators, we introduce an approximate computing method wherein DNN computations are performed block-wise (a block is a group of bits) and re-configurability is supported at the granularity of blocks.

TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems

no code implementations25 Feb 2020 Sourjya Roy, Shrihari Sridharan, Shubham Jain, Anand Raghunathan

To address this challenge, there is a need for tools that can model the functional impact of non-idealities on DNN training and inference.

TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks

no code implementations15 Sep 2019 Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan

The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs).

Image Classification Language Modelling

RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

no code implementations31 Aug 2018 Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan

We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.

SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks

no code implementations7 Nov 2017 Sanchari Sen, Shubham Jain, Swagath Venkataramani, Anand Raghunathan

SparCE consists of 2 key micro-architectural enhancements- a Sparsity Register File (SpRF) that tracks zero registers and a Sparsity aware Skip Address (SASA) table that indicates instructions to be skipped.

Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications

no code implementations1 Nov 2017 Shubham Jain, Marco Gruteser

Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious.

Material Recognition object-detection +1

2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

no code implementations31 Jul 2017 Jay Patravali, Shubham Jain, Sasank Chilamkurthy

In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac MR image segmentation using Deep Convolutional Neural Networks (CNN).

Image Segmentation Semantic Segmentation

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