Sreyasi Nag Chowdhury


2021

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SANDI: Story-and-Images Alignment
Sreyasi Nag Chowdhury | Simon Razniewski | Gerhard Weikum
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

The Internet contains a multitude of social media posts and other of stories where text is interspersed with images. In these contexts, images are not simply used for general illustration, but are judiciously placed in certain spots of a story for multimodal descriptions and narration. In this work we analyze the problem of text-image alignment, and present SANDI, a methodology for automatically selecting images from an image collection and aligning them with text paragraphs of a story. SANDI combines visual tags, user-provided tags and background knowledge, and uses an Integer Linear Program to compute alignments that are semantically meaningful. Experiments show that SANDI can select and align images with texts with high quality of semantic fit.

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Exploiting Image–Text Synergy for Contextual Image Captioning
Sreyasi Nag Chowdhury | Rajarshi Bhowmik | Hareesh Ravi | Gerard de Melo | Simon Razniewski | Gerhard Weikum
Proceedings of the Third Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Modern web content - news articles, blog posts, educational resources, marketing brochures - is predominantly multimodal. A notable trait is the inclusion of media such as images placed at meaningful locations within a textual narrative. Most often, such images are accompanied by captions - either factual or stylistic (humorous, metaphorical, etc.) - making the narrative more engaging to the reader. While standalone image captioning has been extensively studied, captioning an image based on external knowledge such as its surrounding text remains under-explored. In this paper, we study this new task: given an image and an associated unstructured knowledge snippet, the goal is to generate a contextual caption for the image.

2019

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CITE: A Corpus of Image-Text Discourse Relations
Malihe Alikhani | Sreyasi Nag Chowdhury | Gerard de Melo | Matthew Stone
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. Like previous corpora annotating discourse structure between text arguments, such as the Penn Discourse Treebank, our new corpus aids in establishing a better understanding of natural communication and common-sense reasoning, while our findings have implications for a wide range of applications, such as understanding and generation of multimodal documents.

2016

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Know2Look: Commonsense Knowledge for Visual Search
Sreyasi Nag Chowdhury | Niket Tandon | Gerhard Weikum
Proceedings of the 5th Workshop on Automated Knowledge Base Construction