Search Results for author:
Found 24 papers, 11 papers with code
SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields.
Ranked #1 on
Crop Yield Prediction
on SICKLE
Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
In this work, we explore the usage of an ensemble of deep learning models as our thief model.
Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift
In this work, we propose an ADA strategy, which given a frame, identifies a set of classes that are hardest for the model to predict accurately, thereby recommending semantically meaningful regions to be annotated in a selected frame.
High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production
We also propose a yield prediction strategy that uses time-series data generated based on the observed growing season and the standard seasonal information obtained from Tamil Nadu Agricultural University for the region.
Learning Hierarchy Aware Features for Reducing Mistake Severity
In this paper, we propose a novel approach for learning Hierarchy Aware Features (HAF) that leverages classifiers at each level of the hierarchy that are constrained to generate predictions consistent with the label hierarchy.
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery
The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images.
A Deep Learning Approach To Estimation Using Measurements Received Over a Network
The measurements are communicated over a network as packets, at a rate unknown to the estimator.
Intelligent Camera Selection Decisions for Target Tracking in a Camera Network
The correct candidate cameras, decreases the number of false Re-ID queries as well as the computation time.
HIERMATCH: Leveraging Label Hierarchies for Improving Semi-Supervised Learning
Additionally, HIERMATCH is a generic-approach to improve any semisupervised learning framework, we demonstrate this using our results on recent state-of-the-art techniques MixMatch and FixMatch.
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias
Through a series of experiments, we validate that curating contextually fair data helps make model predictions fair by balancing the true positive rate for the protected class across groups without compromising on the model's overall performance.
Contextual Diversity for Active Learning
Contextual Diversity (CD) hinges on a crucial observation that the probability vector predicted by a CNN for a region of interest typically contains information from a larger receptive field.
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions
Specifically, we devise an ensemble of these generative classifiers that rank-aggregates their predictions via a Borda count-based consensus.
DGSAC: Density Guided Sampling and Consensus
Unlike mode seeking approaches, our model selection algorithms seek to find one representative hypothesis for each genuine structure present in the data.
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation.
Automatic Detection and Recognition of Individuals in Patterned Species
In this work, we develop a framework for automatic detection and recognition of individuals in different patterned species like tigers, zebras and jaguars.
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment.
Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network.
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation.
Primate Face Identification in the Wild
This loss of habitat has skewed the population of several non-human primate species like chimpanzees and macaques and has constrained them to co-exist in close proximity of human settlements, often leading to human-wildlife conflicts while competing for resources.
Geometry of Deep Generative Models for Disentangled Representations
We use several metrics to compare the properties of latent spaces of disentangled representation models in terms of class separability and curvature of the latent-space.
Unique Identification of Macaques for Population Monitoring and Control
Despite loss of natural habitat due to development and urbanization, certain species like the Rhesus macaque have adapted well to the urban environment.
A Reinforcement Learning Approach to Target Tracking in a Camera Network
The task is challenging due to disjoint views and illumination variation in different cameras.
Semi-Supervised Clustering with Neural Networks
Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications.
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.
