2 citations · 3 across the 8 of their papers we have counts for
8 papers
Image Generators are Generalist Vision Learners
Valentin Gabeur, Shangbang Long, Songyou Peng +22
Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language under…
Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
Gautham Govind Anil, Shaan Ul Haque, Nithish Kannen +3
Diffusion models are widely used for generative tasks across domains. Given a pre-trained diffusion model, it is often desirable to fine-tune it further either to correct for error…
Proactive Agents for Multi-Turn Text-to-Image Generation Under Uncertainty
Meera Hahn, Wenjun Zeng, Nithish Kannen +4
User prompts for generative AI models are often underspecified, leading to a misalignment between the user intent and models' understanding. As a result, users commonly have to pai…
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation
Nithish Kannen, Yao Ma, Gerrit J. J. van den Burg +1
News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pret…
Beyond Aesthetics: Cultural Competence in Text-to-Image Models
Nithish Kannen, Arif Ahmad, Marco Andreetto +5
Text-to-Image (T2I) models are being increasingly adopted in diverse global communities where they create visual representations of their unique cultures. Current T2I benchmarks pr…
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction
Rajdeep Mukherjee, Nithish Kannen, Saurabh Kumar Pandey +1
Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come u…
Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases
Nithish Kannen, Udit Sharma, Sumit Neelam +4
Knowledge Base Question Answering (KBQA) systems have the goal of answering complex natural language questions by reasoning over relevant facts retrieved from Knowledge Bases (KB).…
CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form Extraction
Nithish Kannen, Divyanshu Sheth, Abhranil Chandra +1
Acronyms and long-forms are commonly found in research documents, more so in documents from scientific and legal domains. Many acronyms used in such documents are domain-specific a…