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Shraman Pramanick

8 accepted papers

2025

Enrich and Detect: Video Temporal Grounding with Multimodal LLMs

ICCV 2025poster

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 jointly process text and video, in order to effectively localize natural language queries in videos through a two-stage p…

Cited by 0SourcePDFScholar
2024

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

CVPR 2024poster

We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide perform…

2024

Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model

CVPR 2024highlight

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 tuning. However due to the enormous diversity in input-output formats in the vision domain existing general-purpose models…

2024

SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers

NeurIPS 2024poster

Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. We introdu…

2023

EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone

ICCV 2023poster

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 frameworks utilize separate video and language encoders and learn task-specific cross-modal information only during fine-tuning,…

Cited by 98PDFcodeScholar
2023

UniVTG: Towards Unified Video-Language Temporal Grounding

ICCV 2023poster

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., sentences or words), is key for video browsing on social media. Most methods in this direction develop task-specific models that…

Cited by 149PDFcodeScholar
2022

Where in the World Is This Image? Transformer-Based Geo-Localization in the Wild

ECCV 2022poster

"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 diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location de…

2021

MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

EMNLP 2021finding

Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escalation of harmful memes used for trolling, cyberbullying, and abuse. Detecting such memes is challenging as they can be h…