4 papers · 1 filter
Linearizing Vision Transformer with Test-Time Training
Yining Li, Dongchen Han, Zeyu Liu +3
While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such models from scratch remains p…
Linear-Time Global Visual Modeling without Explicit Attention
Ruize He, Dongchen Han, Gao Huang
Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quad…
Vision Transformers are Circulant Attention Learners
Dongchen Han, Tianyu Li, Ziyi Wang +1
The self-attention mechanism has been a key factor in the advancement of vision Transformers. However, its quadratic complexity imposes a heavy computational burden in high-resolut…
ViT: Unlocking Test-Time Training in Vision
Dongchen Han, Yining Li, Tianyu Li +6
Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an online learning problem, construc…