2 citations · 3 across the 6 of their papers we have counts for
6 papers
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval
Zhenyu Ning, Guangda Liu, Qihao Jin +4
Recent developments in Video Large Language Models (Video LLMs) have enabled models to process hour-long videos and exhibit exceptional performance. Nonetheless, the Key-Value (KV)…
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism
Kunyun Wang, Bohan Li, Kai Yu +2
Diffusion models have emerged as a powerful class of generative models across various modalities, including image, video, and audio synthesis. However, their deployment is often li…
FreeKV: Boosting KV Cache Retrieval for Efficient LLM Inference
Guangda Liu, Chengwei Li, Zhenyu Ning +5
Large language models (LLMs) are widely deployed with rapidly expanding context windows to support increasingly demanding applications. However, long contexts pose significant depl…
ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression
Guangda Liu, Chengwei Li, Jieru Zhao +2
Large Language Models (LLMs) have been widely deployed in a variety of applications, and the context length is rapidly increasing to handle tasks such as long-document QA and compl…
SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity
Kunyun Wang, Shuo Yang, Jieru Zhao +4
Deep learning models have become pivotal in the field of video processing and is increasingly critical in practical applications such as autonomous driving and object detection. Al…
Inf-MLLM: Efficient Streaming Inference of Multimodal Large Language Models on a Single GPU
Zhenyu Ning, Jieru Zhao, Qihao Jin +2
Multimodal Large Language Models (MLLMs) are distinguished by their multimodal comprehensive ability and widely used in many real-world applications including GPT-4o, autonomous dr…