no code implementations • 7 Feb 2024 • Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha
Our proposed DM-SFDA method involves fine-tuning a pre-trained text-to-image diffusion model to generate source domain images using features from the target images to guide the diffusion process.
Source-Free Domain Adaptation Unsupervised Domain Adaptation
no code implementations • 2 Oct 2023 • Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha
Domain Adaptation (DA) is a method for enhancing a model's performance on a target domain with inadequate annotated data by applying the information the model has acquired from a related source domain with sufficient labeled data.
no code implementations • 29 Sep 2023 • Anay Majee, Suraj Kothawade, Krishnateja Killiamsetty, Rishabh Iyer
In this paper, we introduce the SCoRe (Submodular Combinatorial Representation Learning) framework and propose a family of Submodular Combinatorial Loss functions to overcome these pitfalls in contrastive learning.
no code implementations • 28 Sep 2023 • Ke Yu, Stephen Albro, Giulia Desalvo, Suraj Kothawade, Abdullah Rashwan, Sasan Tavakkol, Kayhan Batmanghelich, Xiaoqi Yin
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure.
no code implementations • 2 Jun 2023 • Nathan Beck, KrishnaTeja Killamsetty, Suraj Kothawade, Rishabh Iyer
Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to random sampling.
1 code implementation • 18 May 2023 • Nathan Beck, Suraj Kothawade, Pradeep Shenoy, Rishabh Iyer
However, learning unbiased models depends on building a dataset that is representative of a diverse range of realistic scenarios for a given task.
no code implementations • 4 Oct 2022 • Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Lakshman Tamil, Ganesh Ramakrishnan, Rishabh Iyer
It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data.
no code implementations • 4 Oct 2022 • Suraj Kothawade, Akshit Srivastava, Venkat Iyer, Ganesh Ramakrishnan, Rishabh Iyer
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain.
no code implementations • 5 Jul 2022 • Suraj Kothawade, Donna Roy, Michele Fenzi, Elmar Haussmann, Jose M. Alvarez, Christoph Angerer
Existing semantic image retrieval methods often focus on mining for larger sized geographical landmarks, and/or require extra labeled data, such as images/image-pairs with similar objects, for mining images with generic objects.
no code implementations • 17 Jun 2022 • Suraj Kothawade, Shivang Chopra, Saikat Ghosh, Rishabh Iyer
Most approaches assume access to a seed set of instances which contain these rare data instances.
no code implementations • 10 Mar 2022 • Suraj Kothawade, Pavan Kumar Reddy, Ganesh Ramakrishnan, Rishabh Iyer
This issue is further pronounced in SSL methods, as they would use this biased model to obtain psuedo-labels (on the unlabeled data) during training.
1 code implementation • 30 Jan 2022 • Changbin Li, Suraj Kothawade, Feng Chen, Rishabh Iyer
Meta learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks.
1 code implementation • 30 Nov 2021 • Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang, Rishabh Iyer
We propose TALISMAN, a novel framework for Targeted Active Learning or object detectIon with rare slices using Submodular MutuAl iNformation.
no code implementations • 17 Oct 2021 • Suraj Kothawade, Vinaya Khandelwal, Kinjal Basu, Huaduo Wang, Gopal Gupta
That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning.
no code implementations • 10 Oct 2021 • Suraj Kothawade, Anmol Mekala, Chandra Sekhara D, Mayank Kothyari, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi
To address this problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn) that uses Submodular Mutual Information (SMI) functions as acquisition functions to find the most informative set of utterances matching a target accent within a fixed budget.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • NeurIPS 2021 • Suraj Kothawade, Nathan Beck, KrishnaTeja Killamsetty, Rishabh Iyer
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
no code implementations • 30 Apr 2021 • Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data.
1 code implementation • 27 Feb 2021 • Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer
Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is underperforming, and ii)guided summarization, where data (e. g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent.
no code implementations • 26 Jan 2021 • Vishal Kaushal, Suraj Kothawade, Anshul Tomar, Rishabh Iyer, Ganesh Ramakrishnan
For long videos, human reference summaries necessary for supervised video summarization techniques are difficult to obtain.
no code implementations • 16 Oct 2020 • Suraj Kothawade, Jiten Girdhar, Chandrashekhar Lavania, Rishabh Iyer
Unfortunately, these models only learn the relative importance of the different submodular functions (such as diversity, representation or importance), but cannot learn more complex feature representations, which are often required for state-of-the-art performance.
no code implementations • 12 Oct 2020 • Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, Himanshu Asnani, Rishabh Iyer
We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks.
no code implementations • 29 Jul 2020 • Vishal Kaushal, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan
Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea.
no code implementations • 3 Jan 2019 • Vishal Kaushal, Rishabh Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan
This paper addresses automatic summarization of videos in a unified manner.
1 code implementation • 3 Jan 2019 • Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry.
no code implementations • 26 Sep 2018 • Suraj Kothawade, Kunjan Mhaske, Sahil Sharma, Furkhan Shaikh
Satellite Remote Sensing Technology is becoming a major milestone in the prediction of weather anomalies, natural disasters as well as finding alternative resources in proximity using multiple multi-spectral sensors emitting electromagnetic waves at distinct wavelengths.
1 code implementation • 24 Sep 2018 • Rishabh Iyer, Pratik Dubal, Kunal Dargan, Suraj Kothawade, Rohan Mahadev, Vishal Kaushal
With increasing amounts of visual data being created in the form of videos and images, visual data selection and summarization are becoming ever increasing problems.
no code implementations • 24 Sep 2018 • Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan
We propose a novel framework for domain specific video summarization.
no code implementations • 27 May 2018 • Pratik Dubal, Rohan Mahadev, Suraj Kothawade, Kunal Dargan, Rishabh Iyer
To our knowledge, this is the first work which provides a comprehensive evaluation of different deep learning models on various real-world customer deployment scenarios of surveillance video analytics.