5 citations · 10 across the 4 of their papers we have counts for
5 papers
Revealing Occlusions with 4D Neural Fields
Basile Van Hoorick, Purva Tendulkar, Didac Suris +3
For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimat…
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency
Sameer Dharur, Purva Tendulkar, Dhruv Batra +2
Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world -- they answer seemingly difficult qu…
Feel The Music: Automatically Generating A Dance For An Input Song
Purva Tendulkar, Abhishek Das, Aniruddha Kembhavi +1
We present a general computational approach that enables a machine to generate a dance for any input music. We encode intuitive, flexible heuristics for what a 'good' dance is: the…
SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions
Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh +4
Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, propertie…
Trick or TReAT: Thematic Reinforcement for Artistic Typography
Purva Tendulkar, Kalpesh Krishna, Ramprasaath R. Selvaraju +1
An approach to make text visually appealing and memorable is semantic reinforcement - the use of visual cues alluding to the context or theme in which the word is being used to rei…