Search Results for author: Maneesh Singh

Found 35 papers, 14 papers with code

Geometry-biased Transformers for Novel View Synthesis

no code implementations11 Jan 2023 Naveen Venkat, Mayank Agarwal, Maneesh Singh, Shubham Tulsiani

While this representation yields (coarsely) accurate images corresponding to novel viewpoints, the lack of geometric reasoning limits the quality of these outputs.

Novel View Synthesis

Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

no code implementations7 Aug 2022 Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh, R. Venkatesh Babu

The quality of the generated images is predicated on two assumptions; (a) The richness of the hierarchical representations learnt by the generator, and, (b) The linearity and smoothness of the style spaces.

Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021

no code implementations11 Jun 2022 Deepak Mittal, Amir H. Poorjam, Debottam Dutta, Debarpan Bhattacharya, Zemin Yu, Sriram Ganapathy, Maneesh Singh

This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge.

Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features

no code implementations2 Jun 2022 Chieh Hubert Lin, Hsin-Ying Lee, Hung-Yu Tseng, Maneesh Singh, Ming-Hsuan Yang

Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks.

DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning

1 code implementation25 Mar 2022 Sreyan Ghosh, Ashish Seth, and Deepak Mittal, Maneesh Singh, S. Umesh

Inspired by the recent progress in self-supervised learning for computer vision, in this paper we introduce DeLoRes, a new general-purpose audio representation learning approach.

Representation Learning Self-Supervised Learning +1

AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation

1 code implementation CVPR 2022 Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, Shubham Tulsiani

This enables us to represent distributions over 3D shapes conditioned on information from an arbitrary set of spatially anchored query locations and thus perform shape completion in such arbitrary settings (e. g., generating a complete chair given only a view of the back leg).

Permutation Invariant Representations with Applications to Graph Deep Learning

no code implementations14 Mar 2022 Radu Balan, Naveed Haghani, Maneesh Singh

In turn, this proves that almost any classifier can be implemented with an arbitrary small loss of performance.

Class Incremental Online Streaming Learning

no code implementations20 Oct 2021 Soumya Banerjee, Vinay Kumar Verma, Toufiq Parag, Maneesh Singh, Vinay P. Namboodiri

We propose a novel approach (CIOSL) for the class-incremental learning in an \emph{online streaming setting} to address these challenges.

Class Incremental Learning Incremental Learning +1

SpliceOut: A Simple and Efficient Audio Augmentation Method

no code implementations30 Sep 2021 Arjit Jain, Pranay Reddy Samala, Deepak Mittal, Preethi Jyoti, Maneesh Singh

Time masking has become a de facto augmentation technique for speech and audio tasks, including automatic speech recognition (ASR) and audio classification, most notably as a part of SpecAugment.

Audio Classification Automatic Speech Recognition +5

Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

no code implementations2 Aug 2021 Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Shrivastava, Maneesh Singh, Vivek Srikumar

Our experiments demonstrate that a RoBERTa-based model, representative of the current state-of-the-art, fails at reasoning on the following counts: it (a) ignores relevant parts of the evidence, (b) is over-sensitive to annotation artifacts, and (c) relies on the knowledge encoded in the pre-trained language model rather than the evidence presented in its tabular inputs.

Language Modelling

Deep Implicit Surface Point Prediction Networks

no code implementations ICCV 2021 Rahul Venkatesh, Tejan Karmali, Sarthak Sharma, Aurobrata Ghosh, R. Venkatesh Babu, László A. Jeni, Maneesh Singh

Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.

Learning to Stylize Novel Views

1 code implementation ICCV 2021 Hsin-Ping Huang, Hung-Yu Tseng, Saurabh Saini, Maneesh Singh, Ming-Hsuan Yang

Second, we develop point cloud aggregation modules to gather the style information of the 3D scene, and then modulate the features in the point cloud with a linear transformation matrix.

Novel View Synthesis

DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces

no code implementations4 Nov 2020 Rahul Venkatesh, Sarthak Sharma, Aurobrata Ghosh, Laszlo Jeni, Maneesh Singh

Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.

3D Shape Representation

ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours

no code implementations23 May 2020 VSR Veeravasarapu, Abhishek Goel, Deepak Mittal, Maneesh Singh

Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned.

End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition

1 code implementation6 Dec 2019 Shaofei Wang, Vishnu Lokhande, Maneesh Singh, Konrad Kording, Julian Yarkony

These algorithms can efficiently and exactly solve sub-problems and directly optimize a convex upper bound of the real problem, providing optimality certificates on the way.

Image Segmentation Semantic Segmentation

Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models

no code implementations26 Oct 2019 Prashnna K Gyawali, Rudra Shah, Linwei Wang, VSR Veeravasarapu, Maneesh Singh

Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations.

Sampling Bias in Deep Active Classification: An Empirical Study

2 code implementations IJCNLP 2019 Ameya Prabhu, Charles Dognin, Maneesh Singh

The exploding cost and time needed for data labeling and model training are bottlenecks for training DNN models on large datasets.

Active Learning General Classification +2

To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics

no code implementations11 Aug 2019 Aurobrata Ghosh, Zheng Zhong, Steve Cruz, Subbu Veeravasarapu, Terrance E. Boult, Maneesh Singh

We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks.

Image Forensics Representation Learning +1

DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

4 code implementations2 May 2019 Hsin-Ying Lee, Hung-Yu Tseng, Qi Mao, Jia-Bin Huang, Yu-Ding Lu, Maneesh Singh, Ming-Hsuan Yang

In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.

Image-to-Image Translation Perceptual Distance +2

Massively Parallel Benders Decomposition for Correlation Clustering

no code implementations15 Feb 2019 Margret Keuper, Jovita Lukasik, Maneesh Singh, Julian Yarkony

We tackle the problem of graph partitioning for image segmentation using correlation clustering (CC), which we treat as an integer linear program (ILP).

Clustering graph partitioning +2

On Lipschitz Bounds of General Convolutional Neural Networks

no code implementations4 Aug 2018 Dongmian Zou, Radu Balan, Maneesh Singh

Many convolutional neural networks (CNNs) have a feed-forward structure.

Disconnected Manifold Learning for Generative Adversarial Networks

1 code implementation NeurIPS 2018 Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh

Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative Adversarial Networks (GANs).

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

5 code implementations ECCV 2018 Ananya Harsh Jha, Saket Anand, Maneesh Singh, V. S. R. Veeravasarapu

Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.

Data Augmentation

Lipschitz Properties for Deep Convolutional Networks

no code implementations18 Jan 2017 Radu Balan, Maneesh Singh, Dongmian Zou

In this paper we discuss the stability properties of convolutional neural networks.

General Classification

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