Search Results for author: Zihang Meng

Found 11 papers, 4 papers with code

Object-Centric Unsupervised Image Captioning

no code implementations2 Dec 2021 Zihang Meng, David Yang, Xuefei Cao, Ashish Shah, Ser-Nam Lim

Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don't belong to the same image.

Image Captioning

Neural TMDlayer: Modeling Instantaneous flow of features via SDE Generators

1 code implementation ICCV 2021 Zihang Meng, Vikas Singh, Sathya N. Ravi

We study how stochastic differential equation (SDE) based ideas can inspire new modifications to existing algorithms for a set of problems in computer vision.

Data Augmentation Few-Shot Image Classification

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

no code implementations NeurIPS 2021 Zihang Meng, Rudrasis Chakraborty, Vikas Singh

We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) using a reparametrization of the projection matrices.

Connecting What to Say With Where to Look by Modeling Human Attention Traces

1 code implementation CVPR 2021 Zihang Meng, Licheng Yu, Ning Zhang, Tamara Berg, Babak Damavandi, Vikas Singh, Amy Bearman

Learning the grounding of each word is challenging, due to noise in the human-provided traces and the presence of words that cannot be meaningfully visually grounded.

Image Captioning Visual Grounding

Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center

no code implementations31 Jan 2021 Teja Kanchinadam, Zihang Meng, Joseph Bockhorst, Vikas Singh Kim, Glenn Fung

Customer satisfaction is an important factor in creating and maintaining long-term relationships with customers.

Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs

no code implementations NeurIPS 2021 Zihang Meng, Lopamudra Mukherjee, Vikas Singh, Sathya N. Ravi

We propose a framework which makes it feasible to directly train deep neural networks with respect to popular families of task-specific non-decomposable per- formance measures such as AUC, multi-class AUC, F -measure and others, as well as models such as non-negative matrix factorization.

Stochastic Canonical Correlation Analysis: A Riemannian Approach

no code implementations1 Jan 2021 Zihang Meng, Rudrasis Chakraborty, Vikas Singh

We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) derived via a differential geometric perspective of the underlying optimization task.

Physarum Powered Differentiable Linear Programming Layers and Applications

3 code implementations30 Apr 2020 Zihang Meng, Sathya N. Ravi, Vikas Singh

We describe our development and show the use of our solver in a video segmentation task and meta-learning for few-shot learning.

Few-Shot Learning Semantic Segmentation +3

Fooling Computer Vision into Inferring the Wrong Body Mass Index

no code implementations16 May 2019 Owen Levin, Zihang Meng, Vikas Singh, Xiaojin Zhu

Recently it's been shown that neural networks can use images of human faces to accurately predict Body Mass Index (BMI), a widely used health indicator.

General Classification

Efficient Relative Attribute Learning using Graph Neural Networks

1 code implementation ECCV 2018 Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, Vikas Singh

A sizable body of work on relative attributes provides compelling evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields significant improvements in a wide variety of tasks in vision.

ReabsNet: Detecting and Revising Adversarial Examples

no code implementations21 Dec 2017 Jiefeng Chen, Zihang Meng, Changtian Sun, Wei Tang, Yinglun Zhu

Though deep neural network has hit a huge success in recent studies and applica- tions, it still remains vulnerable to adversarial perturbations which are imperceptible to humans.

General Classification

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