6 citations · 15 across the 12 of their papers we have counts for
12 papers
VoiceLongMemEval: Do Assistants Remember How You Sounded?
Ramit Pahwa, Parivesh Priye, Apoorva Beedu
With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long, continuous, multi-session co…
Audio2Tool: Speak, Call, Act -- A Dataset for Benchmarking Speech Tool Use
Ramit Pahwa, Apoorva Beedu, Parivesh Priye +4
Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmarks lack domain breadth, acousti…
HierSum: A Global and Local Attention Mechanism for Video Summarization
Apoorva Beedu, Irfan Essa
Video summarization creates an abridged version (i.e., a summary) that provides a quick overview of the video while retaining pertinent information. In this work, we focus on summa…
Exploring Efficient Foundational Multi-modal Models for Video Summarization
Karan Samel, Apoorva Beedu, Nitish Sontakke +1
Foundational models are able to generate text outputs given prompt instructions and text, audio, or image inputs. Recently these models have been combined to perform tasks on video…
Mamba Fusion: Learning Actions Through Questioning
Zhikang Dong, Apoorva Beedu, Jason Sheinkopf +1
Video Language Models (VLMs) are crucial for generalizing across diverse tasks and using language cues to enhance learning. While transformer-based architectures have been the de f…
Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition -- And Ways to Overcome Them
Harish Haresamudram, Apoorva Beedu, Mashfiqui Rabbi +3
Cross-modal contrastive pre-training between natural language and other modalities, e.g., vision and audio, has demonstrated astonishing performance and effectiveness across a dive…
On the Efficacy of Text-Based Input Modalities for Action Anticipation
Apoorva Beedu, Harish Haresamudram, Karan Samel +1
Anticipating future actions is a highly challenging task due to the diversity and scale of potential future actions; yet, information from different modalities help narrow down pla…
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition
Hyeongju Choi, Apoorva Beedu, Irfan Essa
Human Activity Recognition (HAR) systems have been extensively studied by the vision and ubiquitous computing communities due to their practical applications in daily life, such as…
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition
Hyeongju Choi, Apoorva Beedu, Harish Haresamudram +1
To properly assist humans in their needs, human activity recognition (HAR) systems need the ability to fuse information from multiple modalities. Our hypothesis is that multimodal…
Video based Object 6D Pose Estimation using Transformers
Apoorva Beedu, Huda Alamri, Irfan Essa
We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in…
End-to-End Multimodal Representation Learning for Video Dialog
Huda Alamri, Anthony Bilic, Michael Hu +2
Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance rec…
VideoPose: Estimating 6D object pose from videos
Apoorva Beedu, Zhile Ren, Varun Agrawal +1
We introduce a simple yet effective algorithm that uses convolutional neural networks to directly estimate object poses from videos. Our approach leverages the temporal information…