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MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

Research Efficiency & Systems

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Content 95
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Merged summary

TL;DR - MoE-ViE efficiently scales CLIP-style vision encoders using fine-grained mixture-of-experts designs, specialized load balancing, and an optimized inference kernel. Its largest model matches a state-of-the-art encoder 1.7Ă— larger while using 76% of its latency.

  • Fine-grained MoE topologies consistently outperform dense and standard MoE vision encoders across model sizes.
  • An auxiliary-loss-free balancing method improves expert utilization, while a specialized MoE kernel reduces inference overhead.
  • Frame-level distillation and selective freezing add video capabilities while preserving learned image knowledge.
  • When aligned with an LLM, MoE-ViE leads the compared image and video benchmarks, including against encoders with up to 5Ă— more activated parameters.

Sources (1)

MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

arXiv cs.CV Bonan Zhang, Shiyu Dong, Quan Hung Tran, Katharina Gschwind, Shuqi Yang, Sijia Chen, Adel Ahmadyan, Seungwhan Moon, Lu Zhang, Ahmed Kirmani, Babak Damavandi, Anuj Kumar 2026-08-18 arXiv:2608.17402
Public signals Hugging Face upvotes 19
Providers: Hugging Face · Upvotes 19 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-17 14:32:44.923403 UTC

TL;DR - MoE-ViE efficiently scales CLIP-style vision encoders using fine-grained mixture-of-experts designs, specialized load balancing, and an optimized inference kernel. Its largest model matches a state-of-the-art encoder 1.7Ă— larger while using 76% of its latency.

  • Fine-grained MoE topologies consistently outperform dense and standard MoE vision encoders across model sizes.
  • An auxiliary-loss-free balancing method improves expert utilization, while a specialized MoE kernel reduces inference overhead.
  • Frame-level distillation and selective freezing add video capabilities while preserving learned image knowledge.
  • When aligned with an LLM, MoE-ViE leads the compared image and video benchmarks, including against encoders with up to 5Ă— more activated parameters.
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