8 papers
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…
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…
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:…
A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
Shashank Gupta, Chaitanya Ahuja, Tsung-Yu Lin +4
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is a po…
Think Then Embed: Generative Context Improves Multimodal Embedding
Xuanming Cui, Jianpeng Cheng, Hong-you Chen +11
There is a growing interest in Universal Multimodal Embeddings (UME), where models are required to generate task-specific representations. While recent studies show that Multimodal…
StreamMem: Query-Agnostic KV Cache Memory for Streaming Video Understanding
Yanlai Yang, Zhuokai Zhao, Satya Narayan Shukla +4
Multimodal large language models (MLLMs) have made significant progress in visual-language reasoning, but their ability to efficiently handle long videos remains limited. Despite r…