23 citations · 23 across the 10 of their papers we have counts for
14 papers
Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication
Yuxuan Chen, Jiao Bie, Hang Qi +1
Hybrid federated semantic communication enables user equipment (UEs) to contribute model updates or semantic features under a shared training framework. However, heterogeneous role…
6Bit-Diffusion: Inference-Time Mixed-Precision Quantization for Video Diffusion Models
Rundong Su, Jintao Zhang, Zhihang Yuan +3
Diffusion transformers have demonstrated remarkable capabilities in generating videos. However, their practical deployment is severely constrained by high memory usage and computat…
VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models
Hanling Zhang, Yayu Zhou, Tongcheng Fang +4
Small Language Models (SLMs) provide computational advantages in resource-constrained environments, yet memory limitations remain a critical bottleneck for edge device deployment.…
Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance
Songsheng Wang, Rucheng Yu, Zhihang Yuan +4
Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs). However, VLMs' significant parameter size…
R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing
Tianyu Fu, Yi Ge, Yichen You +6
Large Language Models (LLMs) achieve impressive reasoning capabilities at the cost of substantial inference overhead, posing substantial deployment challenges. Although distilled S…
VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate
Zhihang Yuan, Rui Xie, Yuzhang Shang +5
Diffusion Transformer(DiT)-based generation models have achieved remarkable success in video generation. However, their inherent computational demands pose significant efficiency c…
DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation
Zhihang Yuan, Siyuan Wang, Rui Xie +6
In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a training-free paradigm that can make use of adaptive temporal compression in latent space. While existing…
DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers
Hanling Zhang, Rundong Su, Zhihang Yuan +5
Text-to-image generation models, especially Multimodal Diffusion Transformers (MMDiT), have shown remarkable progress in generating high-quality images. However, these models often…
E-CAR: Efficient Continuous Autoregressive Image Generation via Multistage Modeling
Zhihang Yuan, Yuzhang Shang, Hanling Zhang +7
Recent advances in autoregressive (AR) models with continuous tokens for image generation show promising results by eliminating the need for discrete tokenization. However, these m…
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization
Rui Xie, Tianchen Zhao, Zhihang Yuan +5
Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, c…
CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios
Luning Wang, Shiyao Li, Xuefei Ning +4
Large Language Models (LLMs) have been widely adopted to process long-context tasks. However, the large memory overhead of the key-value (KV) cache poses significant challenges in…
DiTFastAttn: Attention Compression for Diffusion Transformer Models
Zhihang Yuan, Hanling Zhang, Pu Lu +6
Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to the quadratic complexity of self-attention operators. We propose DiTFastAt…
SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
Haojie Duanmu, Zhihang Yuan, Xiuhong Li +3
Large language models (LLMs) can now handle longer sequences of tokens, enabling complex tasks like book understanding and generating lengthy novels. However, the key-value (KV) ca…
A Survey on Efficient Inference for Large Language Models
Zixuan Zhou, Xuefei Ning, Ke Hong +12
Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirem…