Search Results for author:
Found 11 papers, 1 papers with code
Parameter-Efficient Active Learning for Foundational models
Foundational vision transformer models have shown impressive few shot performance on many vision tasks.
Mitigating Sampling Bias and Improving Robustness in Active Learning
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Robust Contrastive Active Learning with Feature-guided Query Strategies
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data
PS-NOC is agnostic to model architecture, and primarily focuses on the training approach that uses existing fully paired image-caption data and the images with only the novel object detection labels (partially paired data).
Data augmentation to improve robustness of image captioning solutions
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation in solution performance as blur intensity increases.
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
The "baseline" for the policy-gradients in B-SCST is generated by averaging predictive quality metrics (CIDEr-D) of the captions drawn from the distribution obtained using a Bayesian DNN model.
Deep Probabilistic Models to Detect Data Poisoning Attacks
In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions.
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference
In the multimodal setting, the proposed framework improved precision-recall AUC by 10. 2% on the subset of MiT dataset as compared to non-Bayesian baseline.
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks.
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
In the multimodal setting, the proposed framework improved precision-recall AUC by 10. 2% on the subset of MiT dataset as compared to non-Bayesian baseline.
BAR: Bayesian Activity Recognition using variational inference
We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to conventional DNNs.
