11 citations · 20 across the 16 of their papers we have counts for
8 papers · 1 filter
SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation
Yufei Jiao, Jinfa Huang, Zeyue Xue +5
Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existing methods typically apply a…
YOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family
Xu Lin, WenJie Nie, Jinlong Peng +4
Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specif…
OmniScope: Modality-Decoupled Token Compression for Omnimodal Large Language Models
Jinsen Su, Yongdong Luo, Yuexiao Ma +3
Existing token compression methods for omnimodal large language models typically rely on one modality to determine what to retain in the other. We show that this assumption often b…
Motion-Aware Caching for Efficient Autoregressive Video Generation
Jing Xu, Yuexiao Ma, Xuzhe Zheng +7
Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential i…
HEART: Exploiting Head Heterogeneity in Sparse Attention for Video Diffusion
Xuzhe Zheng, Yuexiao Ma, Jing Xu +3
Sparse attention accelerates video diffusion by allowing each attention head to focus on only a small subset of interactions. Existing methods already construct head-specific spars…
Flow caching for autoregressive video generation
Yuexiao Ma, Xuzhe Zheng, Jing Xu +9
Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. Howeve…
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
Yuexiao Ma, Huixia Li, Xiawu Zheng +6
Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We ar…
An Information Theory-inspired Strategy for Automatic Network Pruning
Xiawu Zheng, Yuexiao Ma, Teng Xi +6
Despite superior performance on many computer vision tasks, deep convolution neural networks are well known to be compressed on devices that have resource constraints. Most existin…