Publications (9)
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…
How Video Meetings Change Your Expression
Sumit Sarin, Utkarsh Mall, Purva Tendulkar +1
Do our facial expressions change when we speak over video calls? Given two unpaired sets of videos of people, we seek to automatically find spatio-temporal patterns that are distin…
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…
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…
Affective Faces for Goal-Driven Dyadic Communication
Scott Geng, Revant Teotia, Purva Tendulkar +2
We introduce a video framework for modeling the association between verbal and non-verbal communication during dyadic conversation. Given the input speech of a speaker, our approac…
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…
FLEX: Full-Body Grasping Without Full-Body Grasps
Purva Tendulkar, DÃdac SurÃs, Carl Vondrick
Synthesizing 3D human avatars interacting realistically with a scene is an important problem with applications in AR/VR, video games and robotics. Towards this goal, we address the…
Landscape Learning for Neural Network Inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar +2
Many machine learning methods operate by inverting a neural network at inference time, which has become a popular technique for solving inverse problems in computer vision, robotic…
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…