StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context
  Modeling
Vision-and-Language Navigation (VLN) in real-world settings requires agents
to process continuous visual streams and generate actions with low latency
grounded in language instructions. While Video-based Large Language Models
(Video-LLMs) have driven recent progress, current VLN methods based on
Video-LLM often face trade-offs among fine-grained visual understanding,
long-term context modeling and computational efficiency. We introduce
StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context
modeling strategy to support multi-modal reasoning over interleaved vision,
language and action inputs. The fast-streaming dialogue context facilitates
responsive action generation through a sliding-window of active dialogues,
while the slow-updating memory context compresses historical visual states
using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN
achieves coherent multi-turn dialogue through efficient KV cache reuse,
supporting long video streams with bounded context size and inference cost.
Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with
stable low latency, ensuring robustness and efficiency in real-world
deployment. The project page is:
https://streamvln.github.io/{https://streamvln.github.io/}.
686de9d4cb5725779c60b487
Meng Wei
claimed_verified
2025-07-30T09:05:27.201Z
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weimeng
user
mengwei0427
686de9d4cb5725779c60b488
Chenyang Wan
claimed_verified
2025-07-09T08:49:45.781Z
66bb5e6573ce3e3ef046615a
/avatars/cbfb4b4114dc3afd0eb63b43a809ba09.svg
Chenyang Wan
user
cywan
686de9d4cb5725779c60b489
Xiqian Yu
686de9d4cb5725779c60b48a
Tai Wang
686de9d4cb5725779c60b48b
Yuqiang Yang
686de9d4cb5725779c60b48c
Xiaohan Mao
686de9d4cb5725779c60b48d
Chenming Zhu
686de9d4cb5725779c60b48e
Wenzhe Cai
686de9d4cb5725779c60b48f
Hanqing Wang
claimed_verified
2025-07-16T15:06:45.090Z
65d9f09bbcd15bc5cb255fed
/avatars/4ae7c5366a6454e542bf3b3ddefa9c7e.svg
hanqingwang
user
hanqing94
686de9d4cb5725779c60b490
Yilun Chen
686de9d4cb5725779c60b491
Xihui Liu
686de9d4cb5725779c60b492
Jiangmiao Pang
StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context
  Modeling
Vision-and-Language Navigation (VLN) in real-world settings requires agents
to process continuous visual streams and generate actions with low latency
grounded in language instructions. While Video-based Large Language Models
(Video-LLMs) have driven recent progress, current VLN methods based on
Video-LLM often face trade-offs among fine-grained visual understanding,
long-term context modeling and computational efficiency. We introduce
StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context
modeling strategy to support multi-modal reasoning over interleaved vision,
language and action inputs. The fast-streaming dialogue context facilitates
responsive action generation through a sliding-window of active dialogues,
while the slow-updating memory context compresses historical visual states
using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN
achieves coherent multi-turn dialogue through efficient KV cache reuse,
supporting long video streams with bounded context size and inference cost.
Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with
stable low latency, ensuring robustness and efficiency in real-world
deployment. The project page is:
https://streamvln.github.io/{https://streamvln.github.io/}.
StreamVLN, a streaming VLN framework, uses a hybrid slow-fast context modeling strategy to achieve state-of-the-art performance with low latency and efficient resource usage.
Video-LLMs StreamVLN hybrid slow-fast context modeling multi-modal reasoning fast-streaming dialogue context slow-updating memory context 3D-aware token pruning KV cache reuse VLN-CE benchmarks