Search Results for author:
Found 31 papers, 9 papers with code
Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning
In this work, we present the $\lambda$-Harmonic reward function, which provides a reliable reward signal and enables early stopping for faster training and effective regularization.
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis
We present a one-shot text-to-image diffusion model that can generate high-resolution images from natural language descriptions.
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
This paper introduces a novel approach to leverage the generalizability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA).
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation
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.
SCoRe: Submodular Combinatorial Representation Learning
In this paper we introduce the SCoRe (Submodular Combinatorial Representation Learning) framework, a novel approach in representation learning that addresses inter-class bias and intra-class variance.
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Training high-quality instance segmentation models requires an abundance of labeled images with instance masks and classifications, which is often expensive to procure.
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
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.
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
However, learning unbiased models depends on building a dataset that is representative of a diverse range of realistic scenarios for a given task.
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain.
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
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.
Object-Level Targeted Selection via Deep Template Matching
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.
Active Data Discovery: Mining Unknown Data using Submodular Information Measures
Most approaches assume access to a seed set of instances which contain these rare data instances.
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets
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.
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Meta learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks.
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information
We propose TALISMAN, a novel framework for Targeted Active Learning or object detectIon with rare slices using Submodular MutuAl iNformation.
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning
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.
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
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
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.
Submodular Mutual Information for Targeted Data Subset Selection
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data.
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection
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.
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
For long videos, human reference summaries necessary for supervised video summarization techniques are difficult to obtain.
Deep Submodular Networks for Extractive Data Summarization
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.
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures
We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks.
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework
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.
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance
This paper addresses automatic summarization of videos in a unified manner.
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry.
Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas
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.
A Framework towards Domain Specific Video Summarization
We propose a novel framework for domain specific video summarization.
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization
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.
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras
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.
