no code implementations • NAACL (sdp) 2021 • Yash Gupta, Pawan Sasanka Ammanamanchi, Shikha Bordia, Arjun Manoharan, Deepak Mittal, Ramakanth Pasunuru, Manish Shrivastava, Maneesh Singh, Mohit Bansal, Preethi Jyothi
Large pretrained models have seen enormous success in extractive summarization tasks.
1 code implementation • 13 Dec 2024 • Yash Malviya, Karan Dhingra, Maneesh Singh
Regulatory documents are rich in nuanced terminology and specialized semantics.
no code implementations • 11 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.
no code implementations • 7 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.
1 code implementation • 27 Jun 2022 • Debottam Dutta, Debarpan Bhattacharya, Sriram Ganapathy, Amir H. Poorjam, Deepak Mittal, Maneesh Singh
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
no code implementations • 11 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.
no code implementations • 2 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.
2 code implementations • 25 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.
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).
no code implementations • 15 Mar 2022 • Susmit Agrawal, Prabhat Kumar, Siddharth Seth, Toufiq Parag, Maneesh Singh, Venkatesh Babu
Recent algorithms for image manipulation detection almost exclusively use deep network models.
no code implementations • 14 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.
no code implementations • 20 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.
no code implementations • 30 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.
1 code implementation • IJCAI 2021 • Arjit Jain, Pranay Reddy Samala, Preethi Jyothi, Deepak Mittal, Maneesh Singh
The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image.
Image Augmentation
Semi Supervised Learning for Image Captioning
no code implementations • 2 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.
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.
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.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, Mohit Bansal
We introduce HoVer (HOppy VERification), a dataset for many-hop evidence extraction and fact verification.
no code implementations • 4 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.
no code implementations • 23 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.
1 code implementation • 6 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.
no code implementations • 26 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.
1 code implementation • 24 Oct 2019 • Han-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh, Ming-Hsuan Yang
This intermediate domain is constructed by translating the source images to mimic the ones in the target domain.
no code implementations • 25 Sep 2019 • VSR Veeravasarapu, Deepak Mittal, Abhishek Goel, Maneesh Singh
In this work, we devise a curriculum-learning-based training process for object boundary detection.
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.
Ranked #2 on
Text Classification
on Amazon-5
1 code implementation • 12 Sep 2019 • Vishnu Suresh Lokhande, Shaofei Wang, Maneesh Singh, Julian Yarkony
We tackle optimization of weighted set packing by relaxing integrality in our ILP formulation.
2 code implementations • ICCV 2019 • Yufei Ye, Maneesh Singh, Abhinav Gupta, Shubham Tulsiani
We present an approach for pixel-level future prediction given an input image of a scene.
no code implementations • 11 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.
no code implementations • 27 Jun 2019 • Aurobrata Ghosh, Zheng Zhong, Terrance E. Boult, Maneesh Singh
It comprises a novel approach for learning rich filters and for suppressing image-edges.
4 code implementations • 2 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.
no code implementations • 15 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).
no code implementations • 4 Aug 2018 • Dongmian Zou, Radu Balan, Maneesh Singh
Many convolutional neural networks (CNNs) have a feed-forward structure.
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).
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.
1 code implementation • ICCV 2017 • Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, Ming-Hsuan Yang
We present an unsupervised representation learning approach using videos without semantic labels.
Ranked #46 on
Self-Supervised Action Recognition
on HMDB51
no code implementations • 18 Jan 2017 • Radu Balan, Maneesh Singh, Dongmian Zou
In this paper we discuss the stability properties of convolutional neural networks.