14 citations · 32 across the 22 of their papers we have counts for
25 papers
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models
Tripti Shukla, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introd…
Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of independently, in a disentangled fashion, controlling the outputs of text-to-image diffusion models with color and style attributes of a user-supplied re…
AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of customizing text-to-image diffusion models with user-supplied reference images. Given new prompts, the existing methods can capture the key concept from…
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Tripti Shukla +1
We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layo…
Iterative Multi-granular Image Editing using Diffusion Models
K J Joseph, Prateksha Udhayanan, Tripti Shukla +4
Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such…
Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval
Prateksha Udhayanan, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of composed image retrieval that takes an input query consisting of an image and a modification text indicating the desired changes to be made on the image…
CoPL: Contextual Prompt Learning for Vision-Language Understanding
Koustava Goswami, Srikrishna Karanam, Prateksha Udhayanan +2
Recent advances in multimodal learning has resulted in powerful vision-language models, whose representations are generalizable across a variety of downstream tasks. Recently, thei…
Learning with Difference Attention for Visually Grounded Self-supervised Representations
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by the…
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, K J Joseph +3
While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are sev…
What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan +1
Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the ke…
Agent-Specific Deontic Modality Detection in Legal Language
Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan +1
Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties. While natural language unders…
Entailment Relation Aware Paraphrase Generation
Abhilasha Sancheti, Balaji Vasan Srinivasan, Rachel Rudinger
We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward…
CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring
Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan +2
Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents…
IGA : An Intent-Guided Authoring Assistant
Simeng Sun, Wenlong Zhao, Varun Manjunatha +5
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants h…
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
Hrituraj Singh, Gaurav Verma, Aparna Garimella +1
Author stylized rewriting is the task of rewriting an input text in a particular author's style. Recent works in this area have leveraged Transformer-based language models in a den…
Incorporating Stylistic Lexical Preferences in Generative Language Models
Hrituraj Singh, Gaurav Verma, Balaji Vasan Srinivasan
While recent advances in language modeling have resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate…
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus
Navita Goyal, Balaji Vasan Srinivasan, Anandhavelu Natarajan +1
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain…
Reinforced Rewards Framework for Text Style Transfer
Abhilasha Sancheti, Kundan Krishna, Balaji Vasan Srinivasan +1
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved. There has b…
Towards Transparent and Explainable Attention Models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan +3
Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model's predictions. Attention distributions can be c…
Generating summaries tailored to target characteristics
Kushal Chawla, Hrituraj Singh, Arijit Pramanik +2
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries. While there have been attempts to incorporate a few handpicked char…
A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text
Gaurav Verma, Balaji Vasan Srinivasan
With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individu…
Adapting Language Models for Non-Parallel Author-Stylized Rewriting
Bakhtiyar Syed, Gaurav Verma, Balaji Vasan Srinivasan +2
Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the gene…
Let's Ask Again: Refine Network for Automatic Question Generation
Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra +2
In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that…
Improving generation quality of pointer networks via guided attention
Kushal Chawla, Kundan Krishna, Balaji Vasan Srinivasan
Pointer generator networks have been used successfully for abstractive summarization. Along with the capability to generate novel words, it also allows the model to copy from the i…