2 citations · 2 across the 14 of their papers we have counts for
9 papers · 1 filter
MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models
Xuanming Cui, Shlok Kumar Mishra, Wentao Bao +6
Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods eit…
SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
Mingyuan Wu, Jingcheng Yang, Shengyi Qian +11
We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning. For each query, the m…
RepFusion: Leveraging Multimodal Priors for Denoising in Representation Space
Xichen Pan, Aashu Singh, Satya Narayan Shukla +3
Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically limited to text encoding, while denoising is handled by newly trained generative…
Detecting AI-Generated Content on Social Media with Multi-modal Language Models
Chenyang Yang, Shen Yan, Yibo Yang +13
Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, an…
An Attribute-Based Measure of Video Complexity
Aditya Sarkar, Yi Li, Zihao Wang +8
A new framework for the estimation of the complexity posed by video-question pairs to video-LLMs, Video Attribute-Based Complexity (VideoABC), is proposed. Video complexity is defi…
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation
Chao Li, Tianhong Li, Sai Vidyaranya Nuthalapati +9
Unifying text-image contrastive learning and text-to-image (T2I) generation in a single end-to-end model is challenging because the two objectives demand opposing masking regimes:…
Xray-Visual Models: Scaling Vision models on Industry Scale Data
Shlok Mishra, Tsung-Yu Lin, Linda Wang +24
We present Xray-Visual, a unified vision model architecture for large-scale image and video understanding trained on industry-scale social media data. Our model leverages over 15 b…
Socratic Students: Teaching Language Models to Learn by Asking Questions
Rajeev Bhatt Ambati, Tianyi Niu, Aashu Singh +3
Large language Models (LLMs) are usually used to answer questions, but many high-stakes applications (e.g., tutoring, clinical support) require the complementary skill of asking qu…