1 code implementation • EMNLP (ACL) 2021 • Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research.
no code implementations • Findings (EMNLP) 2021 • Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
1 code implementation • PoliticalNLP (LREC) 2022 • Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
no code implementations • 9 Nov 2023 • Ruijie Jiang, Thuan Nguyen, Shuchin Aeron, Prakash Ishwar
For a widely-studied data model and general loss and sample-hardening functions we prove that the Supervised Contrastive Learning (SCL), Hard-SCL (HSCL), and Unsupervised Contrastive Learning (UCL) risks are minimized by representations that exhibit Neural Collapse (NC), i. e., the class means form an Equianglular Tight Frame (ETF) and data from the same class are mapped to the same representation.
1 code implementation • 2 Apr 2023 • Boyang Lyu, Thuan Nguyen, Matthias Scheutz, Prakash Ishwar, Shuchin Aeron
Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions.
no code implementations • 21 Mar 2023 • Zhangchi Lu, Mertcan Cokbas, Prakash Ishwar, Jansuz Konrad
Unobtrusive monitoring of distances between people indoors is a useful tool in the fight against pandemics.
no code implementations • 22 Dec 2022 • Mertcan Cokbas, Prakash Ishwar, Janusz Konrad
Person re-identification (PRID) has been thoroughly researched in typical surveillance scenarios where various scenes are monitored by side-mounted, rectilinear-lens cameras.
1 code implementation • 26 Oct 2022 • Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron
To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i. e., the latent representations that are unchanged and transferable between domains.
no code implementations • 4 Oct 2022 • Mertcan Cokbas, John Bolognino, Janusz Konrad, Prakash Ishwar
Person re-identification (PRID) from side-mounted rectilinear-lens cameras is a well-studied problem.
1 code implementation • 31 Aug 2022 • Ruijie Jiang, Thuan Nguyen, Prakash Ishwar, Shuchin Aeron
In this paper, motivated by the effectiveness of hard-negative sampling strategies in H-UCL and the usefulness of label information in SCL, we propose a contrastive learning framework called hard-negative supervised contrastive learning (H-SCL).
1 code implementation • 1 Aug 2022 • Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron
Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain.
2 code implementations • 25 Jan 2022 • Thuan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron
Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG).
1 code implementation • 4 Nov 2021 • Ruijie Jiang, Prakash Ishwar, Shuchin Aeron
We analyze a novel min-max framework that seeks a representation which minimizes the maximum (worst-case) generalized contrastive learning loss over all couplings (joint distributions between positive and negative samples subject to marginal constraints) and prove that the resulting min-max optimum representation will be degenerate.
no code implementations • 9 Sep 2021 • Christy Lin, Daniel Sussman, Prakash Ishwar
This paper studies properties of random walk based node embeddings in the unsupervised setting of discovering hidden block structure in the network, i. e., learning node representations whose cluster structure in Euclidean space reflects their adjacency structure within the network.
1 code implementation • 4 Sep 2021 • Boyang Lyu, Thuan Nguyen, Prakash Ishwar, Matthias Scheutz, Shuchin Aeron
To bridge this gap between theory and practice, we introduce a new upper bound that is free of terms having such dual dependence, resulting in a fully optimizable risk upper bound for the unseen domain.
1 code implementation • 23 Jan 2021 • M. Ozan Tezcan, Prakash Ishwar, Janusz Konrad
In this work, we introduce spatio-temporal data augmentations and apply them to one of the leading video-agnostic BGS algorithms, BSUV-Net.
2 code implementations • 16 Aug 2020 • Alyssa Smith, David Assefa Tofu, Mona Jalal, Edward Edberg Halim, Yimeng Sun, Vidya Akavoor, Margrit Betke, Prakash Ishwar, Lei Guo, Derry Wijaya
The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents.
no code implementations • ACL 2020 • Afra Feyza Aky{\"u}rek, Lei Guo, R Elanwar, a, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.
1 code implementation • 23 May 2020 • Zhihao Duan, M. Ozan Tezcan, Hayato Nakamura, Prakash Ishwar, Janusz Konrad
Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity.
no code implementations • 12 Apr 2020 • Mertcan Cokbas, Prakash Ishwar, Janusz Konrad
We propose a people counting system which uses a low-resolution thermal sensor.
no code implementations • 2 Mar 2020 • Hiroki Kawai, Jia-Wei Chen, Prakash Ishwar, Janusz Konrad
We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information.
1 code implementation • ICCV 2020 • M. Ozan Tezcan, Prakash Ishwar, Janusz Konrad
In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame.
1 code implementation • 26 Jul 2019 • M. Ozan Tezcan, Prakash Ishwar, Janusz Konrad
In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame.
no code implementations • 21 Jun 2019 • Jiawei Chen, Janusz Konrad, Prakash Ishwar
Specifically, we propose a cyclically-trained adversarial network to learn a mapping from image space to latent representation space and back such that the latent representation is invariant to a specified factor of variation (e. g., identity).
no code implementations • 11 Jan 2019 • Mehrnoosh Sameki, Sha Lai, Kate K. Mays, Lei Guo, Prakash Ishwar, Margrit Betke
We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling.
no code implementations • 19 Mar 2018 • Jiawei Chen, Janusz Konrad, Prakash Ishwar
Reliable facial expression recognition plays a critical role in human-machine interactions.
Facial Expression Recognition
Facial Expression Recognition (FER)
+3
no code implementations • 19 Dec 2017 • Ardhendu Tripathy, Ye Wang, Prakash Ishwar
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
1 code implementation • 9 Nov 2016 • Weicong Ding, Christy Lin, Prakash Ishwar
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data.
no code implementations • 12 Oct 2016 • Jiawei Chen, Jonathan Wu, Janusz Konrad, Prakash Ishwar
Deep convolutional neural networks (ConvNets) have been recently shown to attain state-of-the-art performance for action recognition on standard-resolution videos.
no code implementations • 23 Aug 2015 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties.
no code implementations • 3 Apr 2015 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
Our key algorithmic insight for estimation is to establish a statistical connection between M4 and topic models by viewing pairwise comparisons as words, and users as documents.
no code implementations • 11 Dec 2014 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons.
no code implementations • 2 Dec 2013 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl
We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available.
no code implementations • 30 Oct 2013 • Weicong Ding, Prakash Ishwar, Mohammad H. Rohban, Venkatesh Saligrama
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models.
no code implementations • 29 Jan 2013 • Mohammad Hossein Rohban, Prakash Ishwar, Birant Orten, William C. Karl, Venkatesh Saligrama
We study high-dimensional asymptotic performance limits of binary supervised classification problems where the class conditional densities are Gaussian with unknown means and covariances and the number of signal dimensions scales faster than the number of labeled training samples.