Search Results for author: Jiquan Ngiam

Found 20 papers, 4 papers with code

3D-MAN: 3D Multi-frame Attention Network for Object Detection

no code implementations CVPR 2021 Zetong Yang, Yin Zhou, Zhifeng Chen, Jiquan Ngiam

In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset.

3D Object Detection Autonomous Driving

Pseudo-labeling for Scalable 3D Object Detection

no code implementations2 Mar 2021 Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens

To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.

3D Object Detection Autonomous Vehicles +2

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

1 code implementation NeurIPS 2020 Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.

Transfer Learning

Streaming Object Detection for 3-D Point Clouds

no code implementations ECCV 2020 Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen

This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures.

Action Recognition Autonomous Vehicles +2

End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds

no code implementations15 Oct 2019 Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, Vijay Vasudevan

In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.

3D Object Detection

StarNet: Targeted Computation for Object Detection in Point Clouds

no code implementations29 Aug 2019 Jiquan Ngiam, Benjamin Caine, Wei Han, Brandon Yang, Yuning Chai, Pei Sun, Yin Zhou, Xi Yi, Ouais Alsharif, Patrick Nguyen, Zhifeng Chen, Jonathon Shlens, Vijay Vasudevan

We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.

3D Object Detection Pedestrian Detection +1

Using Videos to Evaluate Image Model Robustness

no code implementations22 Apr 2019 Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le, Jonathon Shlens

Compared to previous studies on adversarial examples and synthetic distortions, natural robustness captures a more diverse set of common image transformations that occur in the natural environment.

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

8 code implementations NeurIPS 2019 Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam

We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks.

Classification General Classification +2

Domain Adaptive Transfer Learning with Specialist Models

no code implementations16 Nov 2018 Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang

Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning.

Ranked #2 on Fine-Grained Image Classification on Stanford Cars (using extra training data)

Domain Adaptation Fine-Grained Image Classification +2

Sparse Filtering

no code implementations NeurIPS 2011 Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng

Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification.

Audio Classification Classification +2

Tiled convolutional neural networks

no code implementations NeurIPS 2010 Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang W. Koh, Quoc V. Le, Andrew Y. Ng

Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture.

Object Recognition

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