325 citations · 461 across the 6 of their papers we have counts for
6 papers
ViT-AdaLA: Adapting Vision Transformers with Linear Attention
Yifan Li, Seunghyun Yoon, Viet Dac Lai +4
Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits…
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Jianlou Si, Honggang Zhang, Chun-Guang Li +4
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods b…
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Jason Kuen, Xiangfei Kong, Zhe Lin +4
It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference c…
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information Inflows
Jason Kuen, Xiangfei Kong, Gang Wang +1
Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers. The connectio…
Recurrent Attentional Networks for Saliency Detection
Jason Kuen, Zhenhua Wang, Gang Wang
Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitat…
Recent Advances in Convolutional Neural Networks
Jiuxiang Gu, Zhenhua Wang, Jason Kuen +9
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Amon…