164 citations · 749 across the 21 of their papers we have counts for
16 papers · 1 filter
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Chenxi Liu, Zhaoqi Leng, Pei Sun +5
Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-…
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Zhaoqi Leng, Mingxing Tan, Chenxi Liu +4
Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can…
Are Labels Necessary for Neural Architecture Search?
Chenxi Liu, Piotr Dollár, Kaiming He +3
Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels. In this…
Identifying Model Weakness with Adversarial Examiner
Michelle Shu, Chenxi Liu, Weichao Qiu +1
Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g.…
Rethinking Normalization and Elimination Singularity in Neural Networks
Siyuan Qiao, Huiyu Wang, Chenxi Liu +2
In this paper, we study normalization methods for neural networks from the perspective of elimination singularity. Elimination singularities correspond to the points on the trainin…
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Siyuan Qiao, Huiyu Wang, Chenxi Liu +2
Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typical…
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff +4
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this pap…
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
Runtao Liu, Chenxi Liu, Yutong Bai +1
Referring object detection and referring image segmentation are important tasks that require joint understanding of visual information and natural language. Yet there has been evid…
Deep Nets: What have they ever done for Vision?
Alan L. Yuille, Chenxi Liu
This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of gro…
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann +7
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learni…
Adversarial Attacks Beyond the Image Space
Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang +5
Generating adversarial examples is an intriguing problem and an important way of understanding the working mechanism of deep neural networks. Most existing approaches generated per…
Few-Shot Image Recognition by Predicting Parameters from Activations
Siyuan Qiao, Chenxi Liu, Wei Shen +1
In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examp…
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Siyuan Qiao, Wei Shen, Weichao Qiu +2
Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimatio…
Recurrent Multimodal Interaction for Referring Image Segmentation
Chenxi Liu, Zhe Lin, Xiaohui Shen +3
In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first mo…
SORT: Second-Order Response Transform for Visual Recognition
Yan Wang, Lingxi Xie, Chenxi Liu +3
In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks. We propose a novel approach named Second-Order Response Trans…
Attention Correctness in Neural Image Captioning
Chenxi Liu, Junhua Mao, Fei Sha +1
Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision. But despite their popularity, the "correct…