Search Results for author: Chenxi Liu

Found 32 papers, 13 papers with code

I3DOD: Towards Incremental 3D Object Detection via Prompting

no code implementations24 Aug 2023 Wenqi Liang, Gan Sun, Chenxi Liu, Jiahua Dong, Kangru Wang

Meanwhile, the current class-incremental 3D object detection methods neglect the relationships between the object localization information and category semantic information and assume all the knowledge of old model is reliable.

3D Object Detection Autonomous Driving +2

MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences

1 code implementation CVPR 2023 Yingwei Li, Charles R. Qi, Yin Zhou, Chenxi Liu, Dragomir Anguelov

The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory.

3D Object Detection Motion Forecasting +1

DEJA VU: Continual Model Generalization For Unseen Domains

2 code implementations25 Jan 2023 Chenxi Liu, Lixu Wang, Lingjuan Lyu, Chen Sun, Xiao Wang, Qi Zhu

To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.

Data Augmentation Domain Generalization

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

no code implementations10 Oct 2022 Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov

Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving.

3D Object Detection Autonomous Driving +2

Multi-Class 3D Object Detection with Single-Class Supervision

no code implementations11 May 2022 Mao Ye, Chenxi Liu, Maoqing Yao, Weiyue Wang, Zhaoqi Leng, Charles R. Qi, Dragomir Anguelov

While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost.

3D Object Detection object-detection

Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement

no code implementations CVPR 2021 Zefan Li, Chenxi Liu, Alan Yuille, Bingbing Ni, Wenjun Zhang, Wen Gao

For a given unsupervised task, we design multilevel tasks and define different learning stages for the deep network.

Fresh, Fair and Energy-Efficient Content Provision in a Private and Cache-Enabled UAV Network

no code implementations25 Feb 2021 Peng Yang, Kun Guo, Xing Xi, Tony Q. S. Quek, Xianbin Cao, Chenxi Liu

Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.

Networking and Internet Architecture Signal Processing

Are Labels Necessary for Neural Architecture Search?

2 code implementations ECCV 2020 Chenxi Liu, Piotr Dollár, Kaiming He, Ross Girshick, Alan Yuille, Saining Xie

Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels.

Neural Architecture Search

Identifying Model Weakness with Adversarial Examiner

no code implementations25 Nov 2019 Michelle Shu, Chenxi Liu, Weichao Qiu, Alan Yuille

Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance.

Autonomous Driving

Rethinking Normalization and Elimination Singularity in Neural Networks

1 code implementation21 Nov 2019 Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille

To address this issue, we propose BatchChannel Normalization (BCN), which uses batch knowledge to avoid the elimination singularities in the training of channel-normalized models.

Image Classification Instance Segmentation +3

V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation

no code implementations6 Jun 2019 Zhuotun Zhu, Chenxi Liu, Dong Yang, Alan Yuille, Daguang Xu

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation.

Image Segmentation Neural Architecture Search +2

CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions

3 code implementations CVPR 2019 Runtao Liu, Chenxi Liu, Yutong Bai, Alan Yuille

Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process.

Image Segmentation object-detection +8

Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach

no code implementations 7 2018 Dongxuan He, Chenxi Liu

In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels.

BIG-bench Machine Learning

Deep Nets: What have they ever done for Vision?

no code implementations10 May 2018 Alan L. Yuille, Chenxi Liu

We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves.


Scene Graph Parsing as Dependency Parsing

1 code implementation NAACL 2018 Yu-Siang Wang, Chenxi Liu, Xiaohui Zeng, Alan Yuille

The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.

Dependency Parsing Image Retrieval +2

Progressive Neural Architecture Search

16 code implementations ECCV 2018 Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy

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 learning and evolutionary algorithms.

Evolutionary Algorithms General Classification +3

Adversarial Attacks Beyond the Image Space

no code implementations CVPR 2019 Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, Alan L. Yuille

Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.

Question Answering Visual Question Answering

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

no code implementations ICCV 2017 Siyuan Qiao, Wei Shen, Weichao Qiu, Chenxi Liu, Alan Yuille

We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range.

Object Proposal Generation

Recurrent Multimodal Interaction for Referring Image Segmentation

1 code implementation ICCV 2017 Chenxi Liu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Alan Yuille

In this paper we are interested in the problem of image segmentation given natural language descriptions, i. e. referring expressions.

Image Segmentation Semantic Segmentation

SORT: Second-Order Response Transform for Visual Recognition

no code implementations ICCV 2017 Yan Wang, Lingxi Xie, Chenxi Liu, Ya zhang, Wenjun Zhang, Alan Yuille

In this paper, we reveal the importance and benefits of introducing second-order operations into deep neural networks.

Attention Correctness in Neural Image Captioning

no code implementations31 May 2016 Chenxi Liu, Junhua Mao, Fei Sha, Alan Yuille

Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision.

Image Captioning

Rent3D: Floor-Plan Priors for Monocular Layout Estimation

no code implementations CVPR 2015 Chenxi Liu, Alexander G. Schwing, Kaustav Kundu, Raquel Urtasun, Sanja Fidler

What sets us apart from past work in layout estimation is the use of floor plans as a source of prior knowledge, as well as localization of each image within a bigger space (apartment).

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