349 citations · 395 across the 4 of their papers we have counts for
5 papers
DeepViT: Towards Deeper Vision Transformer
Daquan Zhou, Bingyi Kang, Xiaojie Jin +5
Vision transformers (ViTs) have been successfully applied in image classification tasks recently. In this paper, we show that, unlike convolution neural networks (CNNs)that can be…
AutoSpace: Neural Architecture Search with Less Human Interference
Daquan Zhou, Xiaojie Jin, Xiaochen Lian +4
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automati…
Neural Architecture Search for Lightweight Non-Local Networks
Yingwei Li, Xiaojie Jin, Jieru Mei +7
Non-Local (NL) blocks have been widely studied in various vision tasks. However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the fol…
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Jieru Mei, Yingwei Li, Xiaochen Lian +4
Search space design is very critical to neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that i…
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
Haonan Yu, Xiaochen Lian, Haichao Zhang +1
Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning. In this paper, we…