20 citations · 47 across the 14 of their papers we have counts for
8 papers · 1 filter
Enrich and Detect: Video Temporal Grounding with Multimodal LLMs
Shraman Pramanick, Effrosyni Mavroudi, Yale Song +3
We introduce ED-VTG, a method for fine-grained video temporal grounding utilizing multi-modal large language models. Our approach harnesses the capabilities of multimodal LLMs to j…
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model
Shraman Pramanick, Guangxing Han, Rui Hou +6
The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tu…
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani +98
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric…
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
Shraman Pramanick, Yale Song, Sayan Nag +5
Video-language pre-training (VLP) has become increasingly important due to its ability to generalize to various vision and language tasks. However, existing egocentric VLP framewor…
UniVTG: Towards Unified Video-Language Temporal Grounding
Kevin Qinghong Lin, Pengchuan Zhang, Joya Chen +5
Video Temporal Grounding (VTG), which aims to ground target clips from videos (such as consecutive intervals or disjoint shots) according to custom language queries (e.g., sentence…
VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment
Shraman Pramanick, Li Jing, Sayan Nag +4
Vision-language pre-training (VLP) has recently proven highly effective for various uni- and multi-modal downstream applications. However, most existing end-to-end VLP methods use…
Where in the World is this Image? Transformer-based Geo-localization in the Wild
Shraman Pramanick, Ewa M. Nowara, Joshua Gleason +2
Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge di…
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection
Shraman Pramanick, Aniket Roy, Vishal M. Patel
Multimodal learning is an emerging yet challenging research area. In this paper, we deal with multimodal sarcasm and humor detection from conversational videos and image-text pairs…