activity
20232026
most citedFrom Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning

2 citations · 4 across the 10 of their papers we have counts for

collaborators

16 papers

cs.CV2026

OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping

Xudong Li, Mengdan Zhang, Peixian Chen +7

Spatial intelligence remains a persistent challenge for Multimodal Large Language Models (MLLMs), as it requires coherent spatial scene representations beyond basic object recognit…

cs.CL2025

LTD-Bench: Evaluating Large Language Models by Letting Them Draw

Liuhao Lin, Ke Li, Zihan Xu +5

Current evaluation paradigms for large language models (LLMs) represent a critical blind spot in AI research--relying on opaque numerical metrics that conceal fundamental limitatio…

cs.CV2025

Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs

Xudong Li, Mengdan Zhang, Peixian Chen +8

Multi-modal Large Language Models (MLLMs) excel at single-image tasks but struggle with multi-image understanding due to cross-modal misalignment, leading to hallucinations (contex…

cs.CL2025

VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language Model

Zuwei Long, Yunhang Shen, Chaoyou Fu +11

With the growing requirement for natural human-computer interaction, speech-based systems receive increasing attention as speech is one of the most common forms of daily communicat…

cs.CV20252 cited

From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning

Yuhui Zeng, Haoxiang Wu, Wenjie Nie +6

Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities. This deficiency arises from their arch…

cs.CV2025

Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuracy

Yunhang Shen, Chaoyou Fu, Shaoqi Dong +14

We introduce Long-VITA, a simple yet effective large multi-modal model for long-context visual-language understanding tasks. It is adept at concurrently processing and analyzing mo…