163 citations · 194 across the 13 of their papers we have counts for
13 papers
EgoMAGIC- An Egocentric Video Field Medicine Dataset for Training Perception Algorithms
Brian VanVoorst, Nicholas Walczak, Christopher Gilleo +9
This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part of DARPA's Perceptually-enabled…
MedGRPO: Multi-Task Reinforcement Learning for Heterogeneous Medical Video Understanding
Yuhao Su, Anwesa Choudhuri, Zhongpai Gao +8
Large vision-language models struggle with medical video understanding, where spatial precision, temporal reasoning, and clinical semantics are critical. To address this, we first…
DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos
Zijia Lu, A S M Iftekhar, Gaurav Mittal +6
Long Video Temporal Grounding (LVTG) aims at identifying specific moments within lengthy videos based on user-provided text queries for effective content retrieval. The approach ta…
BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation
Zijia Lu, Ehsan Elhamifar
We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action. Recent works apply transf…
Exploring the Role of Audio in Video Captioning
Yuhan Shen, Linjie Yang, Longyin Wen +3
Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for…
Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency
Hassan Mahmood, Ehsan Elhamifar
Many real-world applications of image recognition require multi-label learning, whose goal is to find all labels in an image. Thus, robustness of such systems to adversarial image…
Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling
Dat Huynh, Jason Kuen, Zhe Lin +2
Open-vocabulary instance segmentation aims at segmenting novel classes without mask annotations. It is an important step toward reducing laborious human supervision. Most existing…
Compositional Fine-Grained Low-Shot Learning
Dat Huynh, Ehsan Elhamifar
We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. Our key observation is that g…
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar +3
Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to a…
Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained -Minimization
Ehsan Elhamifar, Mahdi Soltanolkotabi, Shankar Sastry
High-dimensional data often lie in low-dimensional subspaces corresponding to different classes they belong to. Finding sparse representations of data points in a dictionary built…
Sparse Subspace Clustering: Algorithm, Theory, and Applications
Ehsan Elhamifar, Rene Vidal
In many real-world problems, we are dealing with collections of high-dimensional data, such as images, videos, text and web documents, DNA microarray data, and more. Often, high-di…
On the Lagrangian Biduality of Sparsity Minimization Problems
Dheeraj Singaraju, Ehsan Elhamifar, Roberto Tron +2
Recent results in Compressive Sensing have shown that, under certain conditions, the solution to an underdetermined system of linear equations with sparsity-based regularization ca…
Block-Sparse Recovery via Convex Optimization
Ehsan Elhamifar, Rene Vidal
Given a dictionary that consists of multiple blocks and a signal that lives in the range space of only a few blocks, we study the problem of finding a block-sparse representation o…