63 citations · 117 across the 8 of their papers we have counts for
9 papers
Modular Visual Question Answering via Code Generation
Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar +6
We present a framework that formulates visual question answering as modular code generation. In contrast to prior work on modular approaches to VQA, our approach requires no additi…
Learning and Verification of Task Structure in Instructional Videos
Medhini Narasimhan, Licheng Yu, Sean Bell +2
Given the enormous number of instructional videos available online, learning a diverse array of multi-step task models from videos is an appealing goal. We introduce a new pre-trai…
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency
Medhini Narasimhan, Arsha Nagrani, Chen Sun +4
YouTube users looking for instructions for a specific task may spend a long time browsing content trying to find the right video that matches their needs. Creating a visual summary…
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Jiashun Wang, Huazhe Xu, Medhini Narasimhan +1
We propose a novel framework for multi-person 3D motion trajectory prediction. Our key observation is that a human's action and behaviors may highly depend on the other persons aro…
CLIP-It! Language-Guided Video Summarization
Medhini Narasimhan, Anna Rohrbach, Trevor Darrell
A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subj…
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures
Medhini Narasimhan, Shiry Ginosar, Andrew Owens +2
We introduce a non-parametric approach for infinite video texture synthesis using a representation learned via contrastive learning. We take inspiration from Video Textures, which…
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
Medhini Narasimhan, Erik Wijmans, Xinlei Chen +4
We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agen…
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
Medhini Narasimhan, Svetlana Lazebnik, Alexander G. Schwing
Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge re…
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering
Medhini Narasimhan, Alexander G. Schwing
Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environme…