Search Results for author: Vijay Vasudevan

Found 26 papers, 10 papers with code

When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

no code implementations9 May 2022 Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs

To help contextualize progress on ImageNet and provide a more meaningful evaluation for today's state-of-the-art models, we manually review and categorize every remaining mistake that a few top models make in order to provide insight into the long-tail of errors on one of the most benchmarked datasets in computer vision.

Image Classification

CoCa: Contrastive Captioners are Image-Text Foundation Models

no code implementations4 May 2022 Jiahui Yu, ZiRui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, Yonghui Wu

We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively.

 Ranked #1 on Image Classification on ImageNet (using extra training data)

Action Classification Image Captioning +9

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

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

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

MnasNet: Platform-Aware Neural Architecture Search for Mobile

16 code implementations CVPR 2019 Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.

Image Classification Neural Architecture Search +1

Parallel Architecture and Hyperparameter Search via Successive Halving and Classification

1 code implementation25 May 2018 Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi

We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC).

Classification General Classification

AutoAugment: Learning Augmentation Policies from Data

24 code implementations24 May 2018 Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le

In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.

Fine-Grained Image Classification Image Augmentation

EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS

no code implementations ICLR 2018 Minh-Thang Luong, David Dohan, Adams Wei Yu, Quoc V. Le, Barret Zoph, Vijay Vasudevan

Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones.

Language Modelling Neural Architecture Search +2

Neural Optimizer Search with Reinforcement Learning

3 code implementations21 Sep 2017 Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures.

Machine Translation reinforcement-learning +1

Neural Optimizer Search using Reinforcement Learning

no code implementations ICML 2017 Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures.

Machine Translation reinforcement-learning +1

Learning Transferable Architectures for Scalable Image Recognition

10 code implementations CVPR 2018 Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le

In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture".

Image Classification Neural Architecture Search

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