6 citations · 15 across the 12 of their papers we have counts for
4 papers · 1 filter
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…