8 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference
Quantong Qiu, Zhiyi Hong, Yi Yang +5
The quadratic computational complexity of standard attention mechanisms presents a severe scalability bottleneck for LLMs in long-context scenarios. While hybrid attention mechanis…
cs.CL2026
Elastic Attention: Test-time Adaptive Sparsity Ratios for Efficient Transformers
Zecheng Tang, Quantong Qiu, Yi Yang +6
The quadratic complexity of standard attention mechanisms poses a significant scalability bottleneck for large language models (LLMs) in long-context scenarios. While hybrid attent…
cs.CV2024★ 8 cited
Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation
Xinyang Huang, Chuang Zhu, Kebin Liu +2
Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large…