2 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
An Event is Worth One Token: Event Tokenization for Industrial-scale LLM Recommendation
Fan Xia, Zhaoheng Zheng, Iman Setayesh +12
LLM-based recommendation has scaled along model capacity and sequence length, yet each position encodes only text, semantic IDs, or a few categorical features, discarding rich user…
NEXT: Reasoning-Driven Video Recommendation via a Vision-Language Model
Yuming Liu, Hongye Yang, Harrison Zhao +5
We present NEXT (Next-interest EXploration Transformer), a reasoning-driven video recommendation framework that reasons over the video a user has just watched, infers the viewer's…
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:…
Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System
Jiang Zhang, Yubo Wang, Wei Chang +14
Approximate nearest neighbor (ANN) search is widely used in the retrieval stage of large-scale recommendation systems. In this stage, candidate items are indexed using their learne…
Reason to Contrast: A Cascaded Multimodal Retrieval Framework
Xuanming Cui, Hong-You Chen, Hao Yu +10
Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed (T…
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…
Principled Synthetic Data Enables the First Scaling Laws for LLMs in Recommendation
Benyu Zhang, Qiang Zhang, Jianpeng Cheng +10
Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are cr…
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…