69 citations · 212 across the 15 of their papers we have counts for
15 papers
On Path to Multimodal Generalist: General-Level and General-Bench
Hao Fei, Yuan Zhou, Juncheng Li +29
The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolv…
Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning
Shengqiong Wu, Hao Fei, Liangming Pan +3
Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant ch…
Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Hao Fei, Shengqiong Wu, Hanwang Zhang +2
Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained inst…
SpeechEE: A Novel Benchmark for Speech Event Extraction
Bin Wang, Meishan Zhang, Hao Fei +5
Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from tex…
Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment
Hao Fei, Shengqiong Wu, Meishan Zhang +3
While pre-training large-scale video-language models (VLMs) has shown remarkable potential for various downstream video-language tasks, existing VLMs can still suffer from certain…
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction
Meishan Zhang, Hao Fei, Bin Wang +4
In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply re…