activity
20202022
most citedContrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification

5 citations · 7 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2022

Look, Read and Ask: Learning to Ask Questions by Reading Text in Images

Soumya Jahagirdar, Shankar Gangisetty, Anand Mishra

We present a novel problem of text-based visual question generation or TextVQG in short. Given the recent growing interest of the document image analysis community in combining tex…

cs.CV20225 cited

Contrastive Multi-View Textual-Visual Encoding: Towards One Hundred Thousand-Scale One-Shot Logo Identification

Nakul Sharma, Abhirama S. Penamakuri, Anand Mishra

In this paper, we study the problem of identifying logos of business brands in natural scenes in an open-set one-shot setting. This problem setup is significantly more challenging…

cs.CV20221 cited

Grounding Scene Graphs on Natural Images via Visio-Lingual Message Passing

Aditay Tripathi, Anand Mishra, Anirban Chakraborty

This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several…

cs.CV20221 cited

COFAR: Commonsense and Factual Reasoning in Image Search

Prajwal Gatti, Abhirama Subramanyam Penamakuri, Revant Teotia +3

One characteristic that makes humans superior to modern artificially intelligent models is the ability to interpret images beyond what is visually apparent. Consider the following…

cs.CV2021

Few-shot Visual Relationship Co-localization

Revant Teotia, Vaibhav Mishra, Mayank Maheshwari +1

In this paper, given a small bag of images, each containing a common but latent predicate, we are interested in localizing visual subject-object pairs connected via the common pred…

cs.CV2020

Sketch-Guided Object Localization in Natural Images

Aditay Tripathi, Rajath R Dani, Anand Mishra +1

We introduce the novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch…