collaborators

6 papers

cs.CV2026

BOOKAGENT: Orchestrating Safety-Aware Visual Narratives via Multi-Agent Cognitive Calibration

Bo Gao, Chang Liu, Yuyang Miao +2

Recent advancements in Large Generative Models (LGMs) have revolutionized multi-modal generation. However, generating illustrated storybooks remains an open challenge, where prior…

cs.AI2026

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…

cs.IR2025

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…

cs.CV2025

ESCA: Contextualizing Embodied Agents via Scene-Graph Generation

Jiani Huang, Amish Sethi, Matthew Kuo +6

Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, existing MLLMs do not reliably capture fine-grained links betwe…

cs.CV2025

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…

cs.CV2025

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…