24 citations · 84 across the 13 of their papers we have counts for
14 papers
SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm
Tianyu Li, Dongchen Han, Zixuan Cao +7
The long-standing tension between Pre- and Post-Norm remains an open problem in Transformer architecture, reflecting a fundamental trade-off between training stability and represen…
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…
Step by Step Network
Dongchen Han, Tianzhu Ye, Zhuofan Xia +4
Scaling up network depth is a fundamental pursuit in neural architecture design, as theory suggests that deeper models offer exponentially greater capability. Benefiting from the r…
Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
Yifan Pu, Jixuan Ying, Qixiu Li +7
Vision Transformers (ViTs) have become a universal backbone for both image recognition and image generation. Yet their Multi-Head Self-Attention (MHSA) layer still performs a quadr…
Bridging the Divide: Reconsidering Softmax and Linear Attention
Dongchen Han, Yifan Pu, Zhuofan Xia +6
Widely adopted in modern Vision Transformer designs, Softmax attention can effectively capture long-range visual information; however, it incurs excessive computational cost when d…
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…
Demystify Mamba in Vision: A Linear Attention Perspective
Dongchen Han, Ziyi Wang, Zhuofan Xia +7
Mamba is an effective state space model with linear computation complexity. It has recently shown impressive efficiency in dealing with high-resolution inputs across various vision…
GSVA: Generalized Segmentation via Multimodal Large Language Models
Zhuofan Xia, Dongchen Han, Yizeng Han +3
Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the ima…
Agent Attention: On the Integration of Softmax and Linear Attention
Dongchen Han, Tianzhu Ye, Yizeng Han +5
The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicabi…
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…
Dynamic Perceiver for Efficient Visual Recognition
Yizeng Han, Dongchen Han, Zeyu Liu +7
Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``eas…
Contrastive Language-Image Pre-Training with Knowledge Graphs
Xuran Pan, Tianzhu Ye, Dongchen Han +2
Recent years have witnessed the fast development of large-scale pre-training frameworks that can extract multi-modal representations in a unified form and achieve promising perform…
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding
Haojun Jiang, Yuanze Lin, Dongchen Han +2
Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches fo…