138 citations · 294 across the 13 of their papers we have counts for
13 papers
Morphing into Hybrid Attention Models
Disen Lan, Jianbin Zheng, Yuxi Ren +5
Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with linear attention. However, the e…
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search
Haichao Zhang, Jiashi Li, Xin Xia +2
Recently, Neural architecture search has achieved great success on classification tasks for mobile devices. The backbone network for object detection is usually obtained on the ima…
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Jiashi Li, Xin Xia, Wei Li +6
Due to the complex attention mechanisms and model design, most existing vision Transformers (ViTs) can not perform as efficiently as convolutional neural networks (CNNs) in realist…
MoCoViT: Mobile Convolutional Vision Transformer
Hailong Ma, Xin Xia, Xing Wang +3
Recently, Transformer networks have achieved impressive results on a variety of vision tasks. However, most of them are computationally expensive and not suitable for real-world mo…
TRT-ViT: TensorRT-oriented Vision Transformer
Xin Xia, Jiashi Li, Jie Wu +4
We revisit the existing excellent Transformers from the perspective of practical application. Most of them are not even as efficient as the basic ResNets series and deviate from th…
Self-Supervised Learning for Recommender Systems: A Survey
Junliang Yu, Hongzhi Yin, Xin Xia +3
In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self…