10 citations · 23 across the 18 of their papers we have counts for
4 papers · 1 filter
Benchmarking Composable Compression Techniques in Mixture-of-Experts LLMs
Afsara Benazir, Chen Chen, Rongxiao Qu +3
Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance, and long-context KV-cache g…
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
Weiming Zhuang, Jiabo Huang, Jingtao Li +4
Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between t…
Empirical Recipes for Efficient and Compact Vision-Language Models
Jiabo Huang, Zhizhong Li, Sina Sajadmanesh +2
Deploying vision-language models (VLMs) in resource-constrained settings demands low latency and high throughput, yet existing compact VLMs often fall short of the inference speedu…
UniCompress: Token Compression for Unified Vision-Language Understanding and Generation
Ziyao Wang, Chen Chen, Jingtao Li +4
Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework.…