NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night

1 code implementation • CVPR 2022 • Xueqing Deng, Peng Wang, Xiaochen Lian, Shawn Newsam

Notably, NightLab contains models at two levels of granularity, i. e. image and regional, and each level is composed of light adaptation and segmentation modules.

Segmentation Self-Driving Cars +1

HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers

1 code implementation • CVPR 2021 • Mingyu Ding, Xiaochen Lian, Linjie Yang, Peng Wang, Xiaojie Jin, Zhiwu Lu, Ping Luo

Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources.

Image Classification Neural Architecture Search +3

DeepViT: Towards Deeper Vision Transformer

5 code implementations • 22 Mar 2021 • Daquan Zhou, Bingyi Kang, Xiaojie Jin, Linjie Yang, Xiaochen Lian, Zihang Jiang, Qibin Hou, Jiashi Feng

In this paper, we show that, unlike convolution neural networks (CNNs)that can be improved by stacking more convolutional layers, the performance of ViTs saturate fast when scaled to be deeper.

Image Classification Representation Learning

AutoSpace: Neural Architecture Search with Less Human Interference

1 code implementation • ICCV 2021 • Daquan Zhou, Xiaojie Jin, Xiaochen Lian, Linjie Yang, Yujing Xue, Qibin Hou, Jiashi Feng

Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction.

Neural Architecture Search

Neural Architecture Search for Lightweight Non-Local Networks

2 code implementations • CVPR 2020 • Yingwei Li, Xiaojie Jin, Jieru Mei, Xiaochen Lian, Linjie Yang, Cihang Xie, Qihang Yu, Yuyin Zhou, Song Bai, Alan Yuille

However, it has been rarely explored to embed the NL blocks in mobile neural networks, mainly due to the following challenges: 1) NL blocks generally have heavy computation cost which makes it difficult to be applied in applications where computational resources are limited, and 2) it is an open problem to discover an optimal configuration to embed NL blocks into mobile neural networks.

Image Classification Neural Architecture Search

AtomNAS: Fine-Grained End-to-End Neural Architecture Search

1 code implementation • ICLR 2020 • Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, Jianchao Yang

We propose a fine-grained search space comprised of atomic blocks, a minimal search unit that is much smaller than the ones used in recent NAS algorithms.

Neural Architecture Search

Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents

1 code implementation • 22 May 2018 • Haonan Yu, Xiaochen Lian, Haichao Zhang, Wei Xu

Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning.

reinforcement-learning Reinforcement Learning (RL) +2

Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency

no code implementations • 9 Jun 2014 • Wenhao Lu, Xiaochen Lian, Alan Yuille

A novel mixture of graphical models is proposed, which dynamically couples the landmarks to a hierarchy of segments.