8 papers · 1 filter
EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation
Jiayu Chen, Xiaoyu Wu, Rongshan Gao +6
Audio-driven video generation (A2V) has achieved promising progress in synthesizing temporally coherent and audio-visually aligned videos, yet its inference remains expensive due t…
When and Where to Look: Adaptive Visual Evidence Scheduling for Efficient Long Video Understanding
Ke Li, Jiayu Chen, Maoliang Li +5
Efficient long-video understanding requires vision--language models (VLMs) to reason over a small number of frames selected as sparse visual evidence. Existing relevance-based meth…
Token Radius Attention for Efficient Video Generation
Jiayu Chen, Zhikun Jiang, Maoliang Li +6
Video Diffusion Transformers (VDiTs) enable high-fidelity generation but incur quadratic cost from dense 3D self-attention. Existing head- and block-level sparse methods share comp…
EcoVideo: Entropy-Orchestrated Video Generation Paradigm in Cloud-Edge Dynamics
Jiayu Chen, Hengyi Zhang, Maoliang Li +5
DiT video generation is latency-intensive due to iterative full-frame denoising, while prior cloud-edge methods largely rely on static inter-step decoupling and cannot leverage int…
Pyramid Forcing: Head-Aware Pyramid KV Cache Policy for High-Quality Long Video Generation
Jiayu Chen, Junbei Tang, Wenbiao Zhao +6
Autoregressive video generation enables streaming and open-ended long video synthesis, but still suffers from long-term degradation caused by accumulated errors. Existing KVCache s…
ToProVAR: Efficient Visual Autoregressive Modeling via Tri-Dimensional Entropy-Aware Semantic Analysis and Sparsity Optimization
Jiayu Chen, Ruoyu Lin, Zihao Zheng +4
Visual Autoregressive(VAR) models enhance generation quality but face a critical efficiency bottleneck in later stages. In this paper, we present a novel optimization framework for…
MIRAGE: Runtime Scheduling for Multi-Vector Image Retrieval with Hierarchical Decomposition
Maoliang Li, Ke Li, Yaoyang Liu +5
To effectively leverage user-specific data, retrieval augmented generation (RAG) is employed in multimodal large language model (MLLM) applications. However, conventional retrieval…
EaqVLA: Encoding-aligned Quantization for Vision-Language-Action Models
Feng Jiang, Zihao Zheng, Xiuping Cui +3
With the development of Embodied Artificial intelligence, the end-to-end control policy such as Vision-Language-Action (VLA) model has become the mainstream. Existing VLA models fa…