most citedFLatten Transformer: Vision Transformer using Focused Linear Attention

15 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators

Yifan Pu, Zhuofan Xia, Jiayi Guo +9

This paper identifies significant redundancy in the query-key interactions within self-attention mechanisms of diffusion transformer models, particularly during the early stages of…

cs.CV2024

UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation

Chaoqun Du, Yulin Wang, Jiayi Guo +3

Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target domain during testing. In reality, this adaptability can be influenced by multiple factors. Researchers ha…

cs.CV20241 cited

DyFADet: Dynamic Feature Aggregation for Temporal Action Detection

Le Yang, Ziwei Zheng, Yizeng Han +4

Recent proposed neural network-based Temporal Action Detection (TAD) models are inherently limited to extracting the discriminative representations and modeling action instances wi…

cs.CV20231 cited

Fine-grained Recognition with Learnable Semantic Data Augmentation

Yifan Pu, Yizeng Han, Yulin Wang +3

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta…

cs.CV202315 cited

FLatten Transformer: Vision Transformer using Focused Linear Attention

Dongchen Han, Xuran Pan, Yizeng Han +2

The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks. Linear attention, on the other hand, offers…