1 citations · 2 across the 5 of their papers we have counts for
7 papers
RLHarness: Co-evolving Procedural Skills with Reinforcement Learning for Long-horizon Multimodal Reasoning
Ziqiao Shang, Zian Xu, Ji-Chen Yan +6
Multimodal reasoning requires models to preserve visual evidence through long decision chains while selecting appropriate procedures across diverse scenarios and rules. When learni…
GraphSkillAA: Attribution-Guided Skill-Graph Updating with Targeted Validation and Rollback
Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo
External skills provide domain knowledge and procedures without updating model parameters, but flat collections obscure skill applicability, dependencies, and composition. Graphs o…
LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models
Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu +5
Spatial reasoning is a cornerstone capability for intelligent systems to perceive and interact with the physical world. However, multimodal large language models (MLLMs) frequently…
MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs
Ziqiao Shang, Ling-Yue Ge, Zian Xu +10
Systematically evaluating Multimodal Large Language Models (MLLMs) is essential for advancing Artificial General Intelligence (AGI). Yet existing benchmarks remain inadequate for r…
Variational Bayesian Personalized Ranking
Bin Liu, Xiaohong Liu, Qin Luo +7
Pairwise learning underpins implicit collaborative filtering, yet its effectiveness is often hindered by sparse supervision, noisy interactions, and popularity-driven exposure bias…
Learning Contrastive Feature Representations for Facial Action Unit Detection
Ziqiao Shang, Bin Liu, Fengmao Lv +3
For the Facial Action Unit (AU) detection task, accurately capturing the subtle facial differences between distinct AUs is essential for reliable detection. Additionally, AU detect…
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction
Ziqiao Shang, Li Yu
Recently how to introduce large amounts of unlabeled facial images in the wild into supervised Facial Action Unit (AU) detection frameworks has become a challenging problem. In thi…