Subhransu Maji

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University of Massachusetts Amherst ∙ Amazon ∙ berkeley college ∙ Toyota Technological Institute at Chicago

Assistant Professor of Computer Science at University of Massachusetts Amherst since 2014, Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC) from 2012-2014, Visiting Researcher at University of Oxford 2013, Visiting Researcher at Johns Hopkins University 2012, PHD Student at UC Berkeley from 2006-2011, Visiting Researcher at Microsoft Research India 2010, Intern at Google 2008, Intern at INRIA 2005.

Featured Co-authors

Ming-Hsuan Yang
114 publications
Rui Wang
105 publications
Jan Kautz
84 publications
Andrea Vedaldi
83 publications
Stefano Soatto
59 publications
Juho Kannala
47 publications
Hang Su
39 publications
Pietro Perona
35 publications
Tommi Jaakkola
35 publications
Manmohan Chandraker
31 publications
Piotr Koniusz
27 publications

Neural Shape Parsers for Constructive Solid Geometry

Constructive Solid Geometry (CSG) is a geometric modeling technique that...
12/22/2019 ∙ by Gopal Sharma, et al. ∙ 26 ∙ share

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A Bayesian Perspective on the Deep Image Prior

The deep image prior was recently introduced as a prior for natural imag...
04/16/2019 ∙ by Zezhou Cheng, et al. ∙ 22 ∙ share

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The Spectral Bias of the Deep Image Prior

The "deep image prior" proposed by Ulyanov et al. is an intriguing prope...
12/18/2019 ∙ by Prithvijit Chakrabarty, et al. ∙ 21 ∙ share

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When Does Self-supervision Improve Few-shot Learning?

We present a technique to improve the generalization of deep representat...
10/08/2019 ∙ by Jong-Chyi Su, et al. ∙ 19 ∙ share

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Active Adversarial Domain Adaptation

We propose an active learning approach for transferring representations ...
04/16/2019 ∙ by Jong-Chyi Su, et al. ∙ 16 ∙ share

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Task2Vec: Task Embedding for Meta-Learning

We introduce a method to provide vectorial representations of visual cla...
02/10/2019 ∙ by Alessandro Achille, et al. ∙ 10 ∙ share

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Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

User generated 3D shapes in online repositories contain rich information...
10/03/2019 ∙ by Gopal Sharma, et al. ∙ 7 ∙ share

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Inferring 3D Shapes from Image Collections using Adversarial Networks

We investigate the problem of learning a probabilistic distribution over...
06/11/2019 ∙ by Matheus Gadelha, et al. ∙ 7 ∙ share

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Boosting Supervision with Self-Supervision for Few-shot Learning

We present a technique to improve the transferability of deep representa...
06/17/2019 ∙ by Jong-Chyi Su, et al. ∙ 6 ∙ share

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Meta-Learning with Differentiable Convex Optimization

Many meta-learning approaches for few-shot learning rely on simple base ...
04/07/2019 ∙ by Kwonjoon Lee, et al. ∙ 4 ∙ share

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Multiresolution Tree Networks for 3D Point Cloud Processing

We present multiresolution tree-structured networks to process point clo...
07/10/2018 ∙ by Matheus Gadelha, et al. ∙ 2 ∙ share

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Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

The problems of shape classification and part segmentation from 3D point...
03/30/2020 ∙ by Matheus Gadelha, et al. ∙ 2 ∙ share

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Improved Bilinear Pooling with CNNs

Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23]...
07/21/2017 ∙ by Tsung-Yu Lin, et al. ∙ 0 ∙ share

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3D Shape Induction from 2D Views of Multiple Objects

In this paper we investigate the problem of inducing a distribution over...
12/18/2016 ∙ by Matheus Gadelha, et al. ∙ 0 ∙ share

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High Dimensional Inference with Random Maximum A-Posteriori Perturbations

This paper presents a new approach, called perturb-max, for high-dimensi...
02/10/2016 ∙ by Tamir Hazan, et al. ∙ 0 ∙ share

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Adapting Models to Signal Degradation using Distillation

Model compression and knowledge distillation have been successfully appl...
04/01/2016 ∙ by Jong-Chyi Su, et al. ∙ 0 ∙ share

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Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because th...
07/09/2015 ∙ by Mircea Cimpoi, et al. ∙ 0 ∙ share

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One-to-many face recognition with bilinear CNNs

The recent explosive growth in convolutional neural network (CNN) resear...
06/03/2015 ∙ by Aruni RoyChowdhury, et al. ∙ 0 ∙ share

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Bilinear CNNs for Fine-grained Visual Recognition

We present a simple and effective architecture for fine-grained visual r...
04/29/2015 ∙ by Tsung-Yu Lin, et al. ∙ 0 ∙ share

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Jointly Learning Multiple Measures of Similarities from Triplet Comparisons

Similarity between objects is multi-faceted and it can be easier for hum...
03/05/2015 ∙ by Liwen Zhang, et al. ∙ 0 ∙ share

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Deep convolutional filter banks for texture recognition and segmentation

Research in texture recognition often concentrates on the problem of mat...
11/25/2014 ∙ by Mircea Cimpoi, et al. ∙ 0 ∙ share

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Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objec...
11/14/2013 ∙ by Mircea Cimpoi, et al. ∙ 0 ∙ share

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Fine-Grained Visual Classification of Aircraft

This paper introduces FGVC-Aircraft, a new dataset containing 10,000 ima...
06/21/2013 ∙ by Subhransu Maji, et al. ∙ 0 ∙ share

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Linearized Additive Classifiers

We revisit the additive model learning literature and adapt a penalized ...
10/05/2011 ∙ by Subhransu Maji, et al. ∙ 0 ∙ share

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Multi-view Convolutional Neural Networks for 3D Shape Recognition

A longstanding question in computer vision concerns the representation o...
05/05/2015 ∙ by Hang Su, et al. ∙ 0 ∙ share

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CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape an...
12/22/2017 ∙ by Gopal Sharma, et al. ∙ 0 ∙ share

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3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into...
12/08/2016 ∙ by Evangelos Kalogerakis, et al. ∙ 0 ∙ share

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SPLATNet: Sparse Lattice Networks for Point Cloud Processing

We present a network architecture for processing point clouds that direc...
02/22/2018 ∙ by Hang Su, et al. ∙ 0 ∙ share

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Shape Generation using Spatially Partitioned Point Clouds

We propose a method to generate 3D shapes using point clouds. Given a po...
07/19/2017 ∙ by Matheus Gadelha, et al. ∙ 0 ∙ share

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3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the...
07/20/2017 ∙ by Zhaoliang Lun, et al. ∙ 0 ∙ share

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Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional netwo...
08/22/2018 ∙ by Tsung-Yu Lin, et al. ∙ 0 ∙ share

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A Deeper Look at 3D Shape Classifiers

We investigate the role of representations and architectures for classif...
09/07/2018 ∙ by Jong-Chyi Su, et al. ∙ 0 ∙ share

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VisemeNet: Audio-Driven Animator-Centric Speech Animation

We present a novel deep-learning based approach to producing animator-ce...
05/24/2018 ∙ by Yang Zhou, et al. ∙ 0 ∙ share

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Visualizing and Describing Fine-grained Categories as Textures

We analyze how categories from recent FGVC challenges can be described b...
07/02/2019 ∙ by Tsung-Yu Lin, et al. ∙ 0 ∙ share

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ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

We propose a novel, end-to-end trainable, deep network called ParSeNet t...
03/26/2020 ∙ by Gopal Sharma, et al. ∙ 0 ∙ share

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