Subhransu Maji

research

∙ 09/20/2023

COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification

We present a set of metrics that utilize vision priors to effectively as...

0 Rangel Daroya, et al. ∙

research

∙ 06/08/2023

LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs

A critical obstacle preventing NeRF models from being deployed broadly i...

0 Zezhou Cheng, et al. ∙

research

∙ 06/05/2023

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling

Many modern applications use computer vision to detect and count objects...

0 Gustavo Perez, et al. ∙

research

∙ 12/13/2022

Accidental Turntables: Learning 3D Pose by Watching Objects Turn

We propose a technique for learning single-view 3D object pose estimatio...

0 Zezhou Cheng, et al. ∙

research

∙ 08/18/2022

MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation

We propose to utilize self-supervised techniques in the 2D domain for fi...

18 Gopal Sharma, et al. ∙

research

∙ 07/24/2022

Cross-Modal 3D Shape Generation and Manipulation

Creating and editing the shape and color of 3D objects require tremendou...

7 Zezhou Cheng, et al. ∙

research

∙ 04/11/2022

Improving Few-Shot Part Segmentation using Coarse Supervision

A significant bottleneck in training deep networks for part segmentation...

8 Oindrila Saha, et al. ∙

research

∙ 03/22/2022

Leveraging Textures in Zero-shot Understanding of Fine-Grained Domains

Textures can be used to describe the appearance of objects in a wide ran...

0 Chenyun Wu, et al. ∙

research

∙ 12/01/2021

GANORCON: Are Generative Models Useful for Few-shot Segmentation?

Advances in generative modeling based on GANs has motivated the communit...

3 Oindrila Saha, et al. ∙

research

∙ 11/23/2021

Semi-Supervised Learning with Taxonomic Labels

We propose techniques to incorporate coarse taxonomic labels to train im...

8 Jong-Chyi Su, et al. ∙

research

∙ 08/03/2021

Domain Adaptor Networks for Hyperspectral Image Recognition

We consider the problem of adapting a network trained on three-channel c...

0 Gustavo Perez, et al. ∙

research

∙ 07/02/2021

On Measuring and Controlling the Spectral Bias of the Deep Image Prior

The deep image prior has demonstrated the remarkable ability that untrai...

18 Zenglin Shi, et al. ∙

research

∙ 06/02/2021

The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

Semi-iNat is a challenging dataset for semi-supervised classification wi...

12 Jong-Chyi Su, et al. ∙

research

∙ 04/01/2021

A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification

We evaluate the effectiveness of semi-supervised learning (SSL) on a rea...

19 Jong-Chyi Su, et al. ∙

research

∙ 03/11/2021

The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

This document describes the details and the motivation behind a new data...

9 Jong-Chyi Su, et al. ∙

research

∙ 01/26/2021

Revisiting Contrastive Learning for Few-Shot Classification

Instance discrimination based contrastive learning has emerged as a lead...

16 Orchid Majumder, et al. ∙

research

∙ 01/21/2021

Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

We present a plug-in replacement for batch normalization (BN) called exp...

9 Zhaowei Cai, et al. ∙

research

∙ 12/16/2020

StarcNet: Machine Learning for Star Cluster Identification

We present a machine learning (ML) pipeline to identify star clusters in...

13 Gustavo Perez, et al. ∙

research

∙ 10/06/2020

Shot in the Dark: Few-Shot Learning with No Base-Class Labels

Few-shot learning aims to learn classifiers for new objects from a small...

18 Zitian Chen, et al. ∙

research

∙ 08/03/2020

PhraseCut: Language-based Image Segmentation in the Wild

We consider the problem of segmenting image regions given a natural lang...

5 Chenyun Wu, et al. ∙

research

∙ 08/03/2020

Describing Textures using Natural Language

Textures in natural images can be characterized by color, shape, periodi...

12 Chenyun Wu, et al. ∙

research

∙ 06/26/2020

Unsupervised Discovery of Object Landmarks via Contrastive Learning

Given a collection of images, humans are able to discover landmarks of t...

5 Zezhou Cheng, et al. ∙

research

∙ 05/02/2020

Exploring and Predicting Transferability across NLP Tasks

Recent advances in NLP demonstrate the effectiveness of training large-s...

0 Tu Vu, et al. ∙

research

∙ 04/24/2020

Detecting and Tracking Communal Bird Roosts in Weather Radar Data

The US weather radar archive holds detailed information about biological...

14 Zezhou Cheng, et al. ∙

research

∙ 04/08/2020

Deep Manifold Prior

We present a prior for manifold structured data, such as surfaces of 3D ...

15 Matheus Gadelha, et al. ∙

research

∙ 04/06/2020

Learning Generative Models of Shape Handles

We present a generative model to synthesize 3D shapes as sets of handles...

7 Matheus Gadelha, et al. ∙

research

∙ 03/30/2020

Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions

The problems of shape classification and part segmentation from 3D point...

2 Matheus Gadelha, et al. ∙

research

∙ 03/26/2020

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

We propose a novel, end-to-end trainable, deep network called ParSeNet t...

0 Gopal Sharma, et al. ∙

research

∙ 12/22/2019

Neural Shape Parsers for Constructive Solid Geometry

Constructive Solid Geometry (CSG) is a geometric modeling technique that...

26 Gopal Sharma, et al. ∙

research

∙ 12/18/2019

The Spectral Bias of the Deep Image Prior

The "deep image prior" proposed by Ulyanov et al. is an intriguing prope...

21 Prithvijit Chakrabarty, et al. ∙

research

∙ 10/08/2019

When Does Self-supervision Improve Few-shot Learning?

We present a technique to improve the generalization of deep representat...

19 Jong-Chyi Su, et al. ∙

research

∙ 10/03/2019

Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation

User generated 3D shapes in online repositories contain rich information...

7 Gopal Sharma, et al. ∙

research

∙ 07/02/2019

Visualizing and Describing Fine-grained Categories as Textures

We analyze how categories from recent FGVC challenges can be described b...

0 Tsung-Yu Lin, et al. ∙

research

∙ 06/17/2019

Boosting Supervision with Self-Supervision for Few-shot Learning

We present a technique to improve the transferability of deep representa...

6 Jong-Chyi Su, et al. ∙

research

∙ 06/11/2019

Inferring 3D Shapes from Image Collections using Adversarial Networks

We investigate the problem of learning a probabilistic distribution over...

7 Matheus Gadelha, et al. ∙

research

∙ 04/16/2019

Active Adversarial Domain Adaptation

We propose an active learning approach for transferring representations ...

16 Jong-Chyi Su, et al. ∙

research

∙ 04/16/2019

A Bayesian Perspective on the Deep Image Prior

The deep image prior was recently introduced as a prior for natural imag...

22 Zezhou Cheng, et al. ∙

research

∙ 04/07/2019

Meta-Learning with Differentiable Convex Optimization

Many meta-learning approaches for few-shot learning rely on simple base ...

4 Kwonjoon Lee, et al. ∙

research

∙ 02/10/2019

Task2Vec: Task Embedding for Meta-Learning

We introduce a method to provide vectorial representations of visual cla...

10 Alessandro Achille, et al. ∙

research

∙ 09/07/2018

A Deeper Look at 3D Shape Classifiers

We investigate the role of representations and architectures for classif...

0 Jong-Chyi Su, et al. ∙

research

∙ 08/22/2018

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional netwo...

0 Tsung-Yu Lin, et al. ∙

research

∙ 07/10/2018

Multiresolution Tree Networks for 3D Point Cloud Processing

We present multiresolution tree-structured networks to process point clo...

2 Matheus Gadelha, et al. ∙

research

∙ 05/24/2018

VisemeNet: Audio-Driven Animator-Centric Speech Animation

We present a novel deep-learning based approach to producing animator-ce...

0 Yang Zhou, et al. ∙

research

∙ 02/22/2018

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

We present a network architecture for processing point clouds that direc...

0 Hang Su, et al. ∙

research

∙ 12/22/2017

CSGNet: Neural Shape Parser for Constructive Solid Geometry

We present a neural architecture that takes as input a 2D or 3D shape an...

0 Gopal Sharma, et al. ∙

research

∙ 07/21/2017

Improved Bilinear Pooling with CNNs

Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23]...

0 Tsung-Yu Lin, et al. ∙

research

∙ 07/20/2017

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

We propose a method for reconstructing 3D shapes from 2D sketches in the...

0 Zhaoliang Lun, et al. ∙

research

∙ 07/19/2017

Shape Generation using Spatially Partitioned Point Clouds

We propose a method to generate 3D shapes using point clouds. Given a po...

0 Matheus Gadelha, et al. ∙

research

∙ 12/18/2016

3D Shape Induction from 2D Views of Multiple Objects

In this paper we investigate the problem of inducing a distribution over...

0 Matheus Gadelha, et al. ∙

research

∙ 12/08/2016

3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into...

0 Evangelos Kalogerakis, et al. ∙

Subhransu Maji

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