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Adobe Media and Data Science Research (MDSR) Laboratory
Adobe Media and Data Science Research (MDSR) Laboratory
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CABINET: Content Relevance based Noise Reduction for Table Question Answering
Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) …
Sohan Patnaik, Heril Changwal, Milan Aggarwal, Sumit Bhatia, Yaman Kumar Singla, Balaji Krishnamurthy
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CABINET: Content Relevance based Noise Reduction for Table Question Answering
Advances in Citation Text Generation: Leveraging Multi-Source Seq2Seq Models and Large Language Models
Citation Text Generation (CTG) in scientific documents often relies on standard summarization techniques, which may not fully capture …
Avinash Anand, Ashwin Nair, Kritarth Prasad, Vrinda Narayan, Naman Lal, Debanjan Mahata, Yaman Kumar Singla, Rajiv Shah
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Align via actions: Learning behavior aligns LLMs with human opinions in zero-shot
Large language models (LLMs) have become ubiquitous in various applications, but aligning them with societal expectations remains …
Aanisha Bhattacharyya, Susmit Aggarwal, Yaman Kumar Singla, Tarun Menta, Nikitha SR, Balaji Krishnamurthy
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All should be equal in the eyes of LMs: Counterfactually Aware Fair Text Generation
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated …
Pragyan Banerjee, Abhinav Java, Surgan Jandial, Simra Shahid, Shaz Furniturewala, Balaji Krishnamurthy, Sumit Bhatia
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Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Shannon and Weaver’s seminal information theory divides communication into three levels: technical, semantic, and effectiveness. While …
Ashmit Khandelwal, Aditya Agrawal, Aanisha Bhattacharyya, Yaman Kumar Singla, Somesh Singh, Uttaran Bhattacharya, Ishita Dasgupta, Stefano Petrangeli, Rajiv Ratn Shah, Changyou Chen, Balaji Krishnamurthy
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Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation
Neural retrieval models (NRMs) have been shown to outperform their statistical counterparts owing to their ability to capture semantic …
Michael Llordes, Debasis Ganguly, Sumit Bhatia, Chirag Agarwal
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INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and …
Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy
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Parameter Efficient Local Implicit Image Function Network for Face Segmentation
Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial …
Mausoom Sarkar, Nikitha SR, Mayur Hemani, Rishabh Jain, Balaji Krishnamurthy
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Parameter Efficient Local Implicit Image Function Network for Face Segmentation
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot
Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They …
Aanisha Bhattacharyya, Yaman Kumar Singla, Balaji Krishnamurthy, Changyou Chen, Rajiv Ratn Shah
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A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot
UMFuse: Unified Multi View Fusion for Human Editing applications
Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications. …
Rishabh Jain, Mayur Hemani, Duygu Ceylan, Krishna Kumar Singh, Jingwan Lu, Mausoom Sarkar, Balaji Krishnamurthy
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UMFuse: Unified Multi View Fusion for Human Editing applications
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