57 citations · 176 across the 14 of their papers we have counts for
14 papers
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…
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…
DAT++: Spatially Dynamic Vision Transformer with Deformable Attention
Zhuofan Xia, Xuran Pan, Shiji Song +2
Transformers have shown superior performance on various vision tasks. Their large receptive field endows Transformer models with higher representation power than their CNN counterp…
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…
Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention
Xuran Pan, Tianzhu Ye, Zhuofan Xia +2
Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing…
Joint Representation Learning for Text and 3D Point Cloud
Rui Huang, Xuran Pan, Henry Zheng +4
Recent advancements in vision-language pre-training (e.g. CLIP) have shown that vision models can benefit from language supervision. While many models using language modality have…
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…
ActiveNeRF: Learning where to See with Uncertainty Estimation
Xuran Pan, Zihang Lai, Shiji Song +1
Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective,…
Vision Transformer with Deformable Attention
Zhuofan Xia, Xuran Pan, Shiji Song +2
Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher representati…
On the Integration of Self-Attention and Convolution
Xuran Pan, Chunjiang Ge, Rui Lu +4
Convolution and self-attention are two powerful techniques for representation learning, and they are usually considered as two peer approaches that are distinct from each other. In…
3D Object Detection with Pointformer
Xuran Pan, Zhuofan Xia, Shiji Song +2
Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer…
Regularizing Deep Networks with Semantic Data Augmentation
Yulin Wang, Gao Huang, Shiji Song +3
Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translati…
Implicit Semantic Data Augmentation for Deep Networks
Yulin Wang, Xuran Pan, Shiji Song +3
In this paper, we propose a novel implicit semantic data augmentation (ISDA) approach to complement traditional augmentation techniques like flipping, translation or rotation. Our…