Search Results for author: Sumit Shekhar

Found 14 papers, 3 papers with code

DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents

no code implementations NAACL 2022 Himanshu Maheshwari, Nethraa Sivakumar, Shelly Jain, Tanvi Karandikar, Vinay Aggarwal, Navita Goyal, Sumit Shekhar

Linearly consuming (via scrolling or navigation through default table of content) these documents is time-consuming and challenging.

"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning

no code implementations1 Jun 2023 Abisek Rajakumar Kalarani, Pushpak Bhattacharyya, Niyati Chhaya, Sumit Shekhar

We exploit context by pretraining our model with datasets of three tasks: news image captioning where the news article is the context, contextual visual entailment, and keyword extraction from the context.

Image Captioning Keyword Extraction +2

Audio Retrieval for Multimodal Design Documents: A New Dataset and Algorithms

no code implementations28 Feb 2023 Prachi Singh, Srikrishna Karanam, Sumit Shekhar

We consider and propose a new problem of retrieving audio files relevant to multimodal design document inputs comprising both textual elements and visual imagery, e. g., birthday/greeting cards.


Interactive Control over Temporal Consistency while Stylizing Video Streams

1 code implementation2 Jan 2023 Sumit Shekhar, Max Reimann, Moritz Hilscher, Amir Semmo, Jürgen Döllner, Matthias Trapp

For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel.

Image Stylization Video Stabilization +1

SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

1 code implementation24 May 2022 Divya Kothandaraman, Sumit Shekhar, Abhilasha Sancheti, Manoj Ghuhan, Tripti Shukla, Dinesh Manocha

SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation.

Active Learning Domain Adaptation +2

Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity

no code implementations9 Mar 2022 Sumit Shekhar, Max Reimann, Amir Semmo, Sebastian Pasewaldt, Jürgen Döllner, Matthias Trapp

For videos captured in the wild, we perform a user study to demonstrate the preference for our method in comparison to state-of-the-art approaches.

Video Enhancement

OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis

no code implementations1 Oct 2021 Sumit Shekhar, Bhanu Prakash Reddy Guda, Ashutosh Chaubey, Ishan Jindal, Avneet Jain

We propose novel rewards to account for class imbalance and user feedback in the annotation interface, to improve the active learning method.

Active Learning document understanding +5

Show and Recall: Learning What Makes Videos Memorable

no code implementations17 Jul 2017 Sumit Shekhar, Dhruv Singal, Harvineet Singh, Manav Kedia, Akhil Shetty

With the explosion of video content on the Internet, there is a need for research on methods for video analysis which take human cognition into account.

Video Summarization

Synthesis-based Robust Low Resolution Face Recognition

no code implementations10 Jul 2017 Sumit Shekhar, Vishal M. Patel, Rama Chellappa

Recognition of low resolution face images is a challenging problem in many practical face recognition systems.

Dictionary Learning Face Recognition

Class Consistent Multi-Modal Fusion With Binary Features

no code implementations CVPR 2015 Ashish Shrivastava, Mohammad Rastegari, Sumit Shekhar, Rama Chellappa, Larry S. Davis

Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time.

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