Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long videos remains challenging, as it requires retrieving relevant frames distributed across segments from a large candidate pool given complex queries. This paper investigates d...
Yiyang Huang, Yi-Tian Zhang, Yi-Zhou Wang et al.· 0 citations
Sparsely activated Mixture-of-Experts (MoE) models increase model capacity without a proportional increase in per-token computation. Dense-to-MoE upcycling reuses pretrained dense models to construct such systems, commonly by copying feed-forward networks (FFNs) into independently trained experts. We introduce MASKerad...
Ming-Yuan Zhang, Yue Bai, Zhong-Ruo Wang et al.· 0 citations
ACBQ is presented, a simple yet effective framework that simultaneously addresses weight–activation joint quantization and extreme low-bit weight quantization and an adaptive cross-block quantization strategy that explicitly accounts for cross-layer dependencies by encouraging consistency across blocks.
Hailing Wang, Jianglin Lu, Yitian Zhang et al.· Annual Meeting of the Associ...· 0 citations
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