activity
20242026
most citedMMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

6 citations · 7 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Hinge Regression Tree: A Newton Method for Oblique Regression Tree Splitting

Hongyi Li, Han Lin, Jun Xu

Oblique decision trees combine the transparency of trees with the power of multivariate decision boundaries, but learning high-quality oblique splits is NP-hard, and practical meth…

cs.LG2026

E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis

Fei Ma, Han Lin, Yifan Xie +4

Emotion recognition from electroencephalography (EEG) signals remains challenging due to high inter-subject variability, limited labeled data, and the lack of interpretable reasoni…

astro-ph.IM2025★ 1 cited

AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy

Jinghang Shi, Xiaoyu Tang, Yang Huang +4

Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…

cs.CV2025

When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding

Yan Shu, Hangui Lin, Yexin Liu +7

Large Multimodal Models (LMMs) have achieved impressive progress in visual perception and reasoning. However, when confronted with visually ambiguous or non-semantic scene text, th…

cs.CV2024★ 6 cited

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Kaining Ying, Fanqing Meng, Jin Wang +19

Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…