5 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…
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…
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…
A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models
Xuanming Cui, Jaiminkumar Ashokbhai Bhoi, Chionh Wei Peng +2
Dynamic Scene Graph Generation (DSGG) aims to capture objects and their evolving relations in videos. Despite recent progress, the practicality and quality of generated scene graph…
AirSketch: Generative Motion to Sketch
Hui Xian Grace Lim, Xuanming Cui, Yogesh S Rawat +1
Illustration is a fundamental mode of human expression and communication. Certain types of motion that accompany speech can provide this illustrative mode of communication. While A…