Search Results for author: Yuning Chai

Found 19 papers, 6 papers with code

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors

no code implementations ICCV 2023 Hongge Chen, Zhao Chen, Gregory P. Meyer, Dennis Park, Carl Vondrick, Ashish Shrivastava, Yuning Chai

We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors.

Autonomous Driving

NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

1 code implementation24 Aug 2023 Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela, Ioannis Gkioulekas, Ashish Shrivastava, Yuning Chai

We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.

Optical Flow Estimation

Efficient Transformer-based 3D Object Detection with Dynamic Token Halting

no code implementations ICCV 2023 Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu

Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.

3D Object Detection Autonomous Vehicles +1

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

no code implementations8 Mar 2022 Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, Dragomir Anguelov

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.

Motion Estimation Motion Forecasting

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 +4

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

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 object-detection +2

FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

3 code implementations CVPR 2019 Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.

Semantic Segmentation Semi-Supervised Video Object Segmentation +1

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