164 citations · 430 across the 26 of their papers we have counts for
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cs.CV2026
Audio-Visual Intelligence in Large Foundation Models
You Qin, Kai Liu, Shengqiong Wu +12
Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that can perceive, generate…
cs.CV2026
Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking
Shengqiong Wu, Bobo Li, Xinkai Wang +6
Unified Vision-Language Models (UVLMs) aim to advance multimodal learning by supporting both understanding and generation within a single framework. However, existing approaches la…
cs.CV2026
UniM: A Unified Any-to-Any Interleaved Multimodal Benchmark
Yanlin Li, Minghui Guo, Kaiwen Zhang +13
In real-world multimodal applications, systems usually need to comprehend arbitrarily combined and interleaved multimodal inputs from users, while also generating outputs in any in…