Search Results for author: Ramprasaath R. Selvaraju

Found 10 papers, 6 papers with code

SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency

1 code implementation NAACL 2021 Sameer Dharur, Purva Tendulkar, Dhruv Batra, Devi Parikh, Ramprasaath R. Selvaraju

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 questions requiring reasoning correctly but get simpler associated sub-questions wrong.

Question Answering Visual Grounding +1

SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions

no code implementations CVPR 2020 Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz, Marco Ribeiro, Besmira Nushi, Ece Kamar

We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting VQA-introspect, a new dataset1 which consists of 238K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split.

Visual Question Answering

Trick or TReAT: Thematic Reinforcement for Artistic Typography

1 code implementation19 Mar 2019 Purva Tendulkar, Kalpesh Krishna, Ramprasaath R. Selvaraju, Devi Parikh

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 reinforce the message (e. g., Google Doodles).

Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

no code implementations ICCV 2019 Ramprasaath R. Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, Devi Parikh

Many vision and language models suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image.

Image Captioning Question Answering +2

Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance

1 code implementation ECCV 2018 Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, Stefan Lee

Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks.

Generalized Zero-Shot Learning

Grad-CAM: Why did you say that?

1 code implementation22 Nov 2016 Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra

We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations.

Image Captioning Visual Question Answering

Counting Everyday Objects in Everyday Scenes

no code implementations CVPR 2017 Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Dhruv Batra, Devi Parikh

In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes.

Object Detection Question Answering +1

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