Search Results for author: Nilesh Kulkarni

Found 9 papers, 4 papers with code

Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?

no code implementations3 May 2021 Alexander Raistrick, Nilesh Kulkarni, David F. Fouhey

At the heart of our approach is the idea of collision replay, where we use examples of a collision to provide supervision for observations at a past frame.

Implicit Mesh Reconstruction from Unannotated Image Collections

no code implementations16 Jul 2020 Shubham Tulsiani, Nilesh Kulkarni, Abhinav Gupta

We present an approach to infer the 3D shape, texture, and camera pose for an object from a single RGB image, using only category-level image collections with foreground masks as supervision.

Articulation-aware Canonical Surface Mapping

1 code implementation CVPR 2020 Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani

We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canonical template shape, and 2) inferring the articulation and pose of the template corresponding to the input image.

On-Device Neural Language Model Based Word Prediction

1 code implementation COLING 2018 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.

Language Modelling Machine Translation +4

Syllable-level Neural Language Model for Agglutinative Language

no code implementations WS 2017 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes.

Language Modelling

An Embedded Deep Learning based Word Prediction

1 code implementation6 Jul 2017 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.

Language Modelling Machine Translation +1

Modeling of the Latent Embedding of Music using Deep Neural Network

no code implementations12 May 2017 Zhou Xing, Eddy Baik, Yan Jiao, Nilesh Kulkarni, Chris Li, Gautam Muralidhar, Marzieh Parandehgheibi, Erik Reed, Abhishek Singhal, Fei Xiao, Chris Pouliot

These latent embeddings can be used either as features to feed to subsequent models, such as collaborative filtering, or to build similarity metrics between songs, or to classify music based on the labels for training such as genre, mood, sentiment, etc.

Collaborative Filtering

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