4 citations · 4 across the 4 of their papers we have counts for
5 papers
WinARDet: Compressed Window Auto-Regression for Long-Horizon Vision-Language Grounding
Chenxu Wang, Hangyi Kuang, Bo-Wen Yin +6
MLLM-based detectors formulate vision-language grounding as autoregressive coordinate generation, yet in dense scenes, they often collapse into a box repetition loop, repeatedly em…
CrystaL: Spontaneous Emergence of Visual Latents in MLLMs
Yang Zhang, Danyang Li, Yuxuan Li +4
Multimodal Large Language Models (MLLMs) have achieved remarkable performance by integrating powerful language backbones with large-scale visual encoders. Among these, latent Chain…
Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing
Xiang Li, Xueheng Li, Yu Wang +4
Reinforcement Learning (RL) has empowered Multimodal Large Language Models (MLLMs) to achieve superior human preference alignment in Image Quality Assessment (IQA). However, existi…
Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models
Meng Cao, Pengfei Hu, Yingyao Wang +12
Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in videos remains…
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Jinjin Xu, Liwu Xu, Yuzhe Yang +5
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment…