1 citations · 1 across the 8 of their papers we have counts for
8 papers
ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation
Jiahao Zhao, Xiaomin Yu, Zhongxiang Sun +5
Text-to-image (T2I) models can produce visually compelling images, yet they remain limited on open-world tasks that require complex semantic understanding, multi-step reasoning, an…
Structured Evidence Selection for Weakly Supervised Video Anomaly Detection
Chenglizhao Chen, Tianxiang Nan, Wen Li +4
Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-w…
Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities
Chenglizhao Chen, Yuchen Cao, Xinyu Liu +3
Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbalance. Existing methods genera…
Anisotropic Modality Align
Xiaomin Yu, Yijiang Li, Yuhui Zhang +8
Training multimodal large language models has long been limited by the scarcity of high-quality paired multimodal data. Recent studies show that the shared representation space of…
Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
Xiaomin Yu, Yi Xin, Yuhui Zhang +12
Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of d…
CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model
Feiyang Wang, Xiaomin Yu, Wangyu Wu
Proving Rubik's Cube theorems at the high level represents a notable milestone in human-level spatial imagination and logic thinking and reasoning. Traditional Rubik's Cube robots,…
Text-Only Data Synthesis for Vision Language Model Training
Xiaomin Yu, Wenjie Zhang, Ziyue Qiao +2
Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is costly. In contrast, text data is…
SpikEmo: Enhancing Emotion Recognition With Spiking Temporal Dynamics in Conversations
Xiaomin Yu, Feiyang Wang, Ziyue Qiao
In affective computing, the task of Emotion Recognition in Conversations (ERC) has emerged as a focal area of research. The primary objective of this task is to predict emotional s…