activity
20152021
most citedReducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder

2 citations · 3 across the 5 of their papers we have counts for

collaborators

13 papers

cs.CV2021

Face Age Progression With Attribute Manipulation

Sinzith Tatikonda, Athira Nambiar, Anurag Mittal

Face is one of the predominant means of person recognition. In the process of ageing, human face is prone to many factors such as time, attributes, weather and other subject specif…

eess.IV2021

Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling

Saikat Dutta, Nisarg A. Shah, Anurag Mittal

This paper explores an efficient solution for Space-time Super-Resolution, aiming to generate High-resolution Slow-motion videos from Low Resolution and Low Frame rate videos. A si…

cs.CV2020

Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation

Divya Kothandaraman, Athira Nambiar, Anurag Mittal

Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues. In this paper, we present a novel approach to learn domain adaptive know…

cs.CV2020

MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis

Rahul Chakwate, Arulkumar Subramaniam, Anurag Mittal

Representation learning from 3D point clouds is challenging due to their inherent nature of permutation invariance and irregular distribution in space. Existing deep learning metho…

eess.IV2020

WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution

Vikram Singh, Anurag Mittal

Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving…

cs.CV20202 cited

Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder

Gouthaman KV, Anurag Mittal

Recent studies have shown that current VQA models are heavily biased on the language priors in the train set to answer the question, irrespective of the image. E.g., overwhelmingly…