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20152022
most citedLinguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks

14 citations · 24 across the 13 of their papers we have counts for

collaborators

20 papers

cs.CV2022★ 1 cited

Non-linear Motion Estimation for Video Frame Interpolation using Space-time Convolutions

Saikat Dutta, Arulkumar Subramaniam, Anurag Mittal

Video frame interpolation aims to synthesize one or multiple frames between two consecutive frames in a video. It has a wide range of applications including slow-motion video gener…

cs.CV2021

Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks

Arulkumar Subramaniam, Jayesh Vaidya, Muhammed Abdul Majeed Ameen +2

Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between those regions. Traditionally, this has been done by identifying the ob…

cs.CV2021

On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering

Gouthaman KV, Anurag Mittal

Generalizing beyond the experiences has a significant role in developing practical AI systems. It has been shown that current Visual Question Answering (VQA) models are over-depend…

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…