1 citations · 1 across the 3 of their papers we have counts for
7 papers
COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models
Chenghua Zhu, Zhaolu Kang, Qifan Shi +8
Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottleneck is not only sparse frame sam…
Penny Wise, Pixel Foolish: Bypassing Price Constraints in Multimodal Agents via Visual Adversarial Perturbations
Jiachen Qian, Zhaolu Kang
The rapid proliferation of Multimodal Large Language Models (MLLMs) has enabled mobile agents to execute high-stakes financial transactions, but their adversarial robustness remain…
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Zhaolu Kang, Junhao Gong, Jiaxu Yan +15
Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emp…
Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes
Zhiyuan Feng, Zhaolu Kang, Qijie Wang +16
Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent V…
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models
Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai +8
Multimodal Large Language Models (MLLMs) have demonstrated significant advances across numerous vision-language tasks. MLLMs have shown promising capability in aligning visual and…
JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learning
Zhaolu Kang, Hongtian Cai, Xiangyang Ji +2
In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of Natural Language Processing (NLP) owing to its ability to enhance model gener…