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20172026
most citedProbabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

37 citations · 78 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions

Jiaxiang Jiang, Mahesh Subedar, Omesh Tickoo

Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically underperform on tail classes…

cs.CV2025

Parts-Mamba: Augmenting Joint Context with Part-Level Scanning for Occluded Human Skeleton

Tianyi Shen, Huijuan Xu, Nilesh Ahuja +3

Skeleton action recognition involves recognizing human action from human skeletons. The use of graph convolutional networks (GCNs) has driven major advances in this recognition tas…

cs.CV2025

Windsock is Dancing: Adaptive Multimodal Retrieval-Augmented Generation

Shu Zhao, Tianyi Shen, Nilesh Ahuja +2

Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a promising method to generate factual and up-to-date responses of Multimodal Large Language Models (MLLMs) by incor…

cs.CV2022

Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization

Neslihan Kose, Ranganath Krishnan, Akash Dhamasia +2

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.…

cs.CV20221 cited

FRE: A Fast Method For Anomaly Detection And Segmentation

Ibrahima Ndiour, Nilesh Ahuja, Utku Genc +1

This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem. In this setup, we have access to only anomaly-free training da…

cs.CV2019

Real-time Approximate Bayesian Computation for Scene Understanding

Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez +2

Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable stree…