Search Results for author: Bichen Wu

Found 44 papers, 24 papers with code

Pruning Compact ConvNets for Efficient Inference

no code implementations11 Jan 2023 Sayan Ghosh, Karthik Prasad, Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Graham Cormode, Peter Vajda

The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark.

Network Pruning Neural Architecture Search

INGeo: Accelerating Instant Neural Scene Reconstruction with Noisy Geometry Priors

no code implementations5 Dec 2022 Chaojian Li, Bichen Wu, Albert Pumarola, Peizhao Zhang, Yingyan Lin, Peter Vajda

We present a method that accelerates reconstruction of 3D scenes and objects, aiming to enable instant reconstruction on edge devices such as mobile phones and AR/VR headsets.

Novel View Synthesis

Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference

no code implementations CVPR 2023 Haoran You, Yunyang Xiong, Xiaoliang Dai, Bichen Wu, Peizhao Zhang, Haoqi Fan, Peter Vajda, Yingyan, Lin

Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens.

A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision

no code implementations27 Dec 2021 Ajinkya Tejankar, Maziar Sanjabi, Bichen Wu, Saining Xie, Madian Khabsa, Hamed Pirsiavash, Hamed Firooz

In this paper, we focus on teasing out what parts of the language supervision are essential for training zero-shot image classification models.

Classification Image Captioning +3

Cross-Domain Adaptive Teacher for Object Detection

2 code implementations CVPR 2022 Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, Peter Vajda

To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap.

Data Augmentation Domain Adaptation +2

FBNetV5: Neural Architecture Search for Multiple Tasks in One Run

no code implementations19 Nov 2021 Bichen Wu, Chaojian Li, Hang Zhang, Xiaoliang Dai, Peizhao Zhang, Matthew Yu, Jialiang Wang, Yingyan Lin, Peter Vajda

To tackle these challenges, we propose FBNetV5, a NAS framework that can search for neural architectures for a variety of vision tasks with much reduced computational cost and human effort.

Classification Image Classification +4

Differentiable NAS Framework and Application to Ads CTR Prediction

1 code implementation25 Oct 2021 Ravi Krishna, Aravind Kalaiah, Bichen Wu, Maxim Naumov, Dheevatsa Mudigere, Misha Smelyanskiy, Kurt Keutzer

Neural architecture search (NAS) methods aim to automatically find the optimal deep neural network (DNN) architecture as measured by a given objective function, typically some combination of task accuracy and inference efficiency.

Click-Through Rate Prediction Neural Architecture Search

An Investigation on Hardware-Aware Vision Transformer Scaling

no code implementations29 Sep 2021 Chaojian Li, KyungMin Kim, Bichen Wu, Peizhao Zhang, Hang Zhang, Xiaoliang Dai, Peter Vajda, Yingyan Lin

In particular, when transferred to PiT, our scaling strategies lead to a boosted ImageNet top-1 accuracy of from $74. 6\%$ to $76. 7\%$ ($\uparrow2. 1\%$) under the same 0. 7G FLOPs; and when transferred to the COCO object detection task, the average precision is boosted by $\uparrow0. 7\%$ under a similar throughput on a V100 GPU.

Image Classification object-detection +2

Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

no code implementations18 Apr 2021 Ruizhe Cheng, Bichen Wu, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez

Our model transfers knowledge from pretrained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP.

Zero-Shot Learning

Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning

no code implementations10 Mar 2021 Bernie Wang, Simon Xu, Kurt Keutzer, Yang Gao, Bichen Wu

To address this, we propose a novel self-supervised learning task, which we named Trajectory Contrastive Learning (TCL), to improve meta-training.

Contrastive Learning Meta Reinforcement Learning +3

Unbiased Teacher for Semi-Supervised Object Detection

4 code implementations ICLR 2021 Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda

To address this, we introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner.

Image Classification object-detection +3

FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the Edge

no code implementations25 Nov 2020 Bichen Wu, Qing He, Peizhao Zhang, Thilo Koehler, Kurt Keutzer, Peter Vajda

More efficient variants of FBWave can achieve up to 109x fewer MACs while still delivering acceptable audio quality.

FP-NAS: Fast Probabilistic Neural Architecture Search

no code implementations CVPR 2021 Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli

Furthermore, to search fast in the multi-variate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude.

Neural Architecture Search

ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

no code implementations7 Sep 2020 Sicheng Zhao, Yezhen Wang, Bo Li, Bichen Wu, Yang Gao, Pengfei Xu, Trevor Darrell, Kurt Keutzer

They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains.

Autonomous Driving Domain Adaptation +3

A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

1 code implementation1 Sep 2020 Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer

To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.

Unsupervised Domain Adaptation

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

8 code implementations5 Jun 2020 Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, Peter Vajda

In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships.

General Classification Image Classification +1

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

3 code implementations ECCV 2020 Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, Masayoshi Tomizuka

Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.

3D Semantic Segmentation Point Cloud Segmentation +1

Algorithm-hardware Co-design for Deformable Convolution

2 code implementations19 Feb 2020 Qijing Huang, Dequan Wang, Yizhao Gao, Yaohui Cai, Zhen Dong, Bichen Wu, Kurt Keutzer, John Wawrzynek

In this work, we first investigate the overhead of the deformable convolution on embedded FPGA SoCs, and then show the accuracy-latency tradeoffs for a set of algorithm modifications including full versus depthwise, fixed-shape, and limited-range.

Image Classification Instance Segmentation +4

SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis

1 code implementation16 Jan 2020 Bohan Zhai, Tianren Gao, Flora Xue, Daniel Rothchild, Bichen Wu, Joseph E. Gonzalez, Kurt Keutzer

Automatic speech synthesis is a challenging task that is becoming increasingly important as edge devices begin to interact with users through speech.

Sound Audio and Speech Processing

Domain-Aware Dynamic Networks

no code implementations26 Nov 2019 Tianyuan Zhang, Bichen Wu, Xin Wang, Joseph Gonzalez, Kurt Keutzer

In this work, we propose a method to improve the model capacity without increasing inference-time complexity.

object-detection Object Detection

Efficient Deep Neural Networks

no code implementations20 Aug 2019 Bichen Wu

Model efficiency: we designed neural networks for various computer vision tasks and achieved more than 10x faster speed and lower energy.

Autonomous Driving Domain Adaptation +1

LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

2 code implementations19 Apr 2019 Bernie Wang, Virginia Wu, Bichen Wu, Kurt Keutzer

2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.

Autonomous Vehicles Sensor Fusion

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

1 code implementation CVPR 2019 Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha

We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.

Bayesian Optimization Efficient Neural Network +1

Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

no code implementations ICLR 2019 Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, Kurt Keutzer

Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources.

Neural Architecture Search Quantization

Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

1 code implementation21 Nov 2018 Yifan Yang, Qijing Huang, Bichen Wu, Tianjun Zhang, Liang Ma, Giulio Gambardella, Michaela Blott, Luciano Lavagno, Kees Vissers, John Wawrzynek, Kurt Keutzer

DiracDeltaNet achieves competitive accuracy on ImageNet (88. 7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16.

Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld

no code implementations24 Mar 2018 Sicheng Zhao, Bichen Wu, Joseph Gonzalez, Sanjit A. Seshia, Kurt Keutzer

To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled target domain.

Unsupervised Domain Adaptation

SqueezeNext: Hardware-Aware Neural Network Design

6 code implementations23 Mar 2018 Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, Kurt Keutzer

One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks.

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

13 code implementations4 Dec 2016 Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer

In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.

Autonomous Driving object-detection +1

Shallow Networks for High-Accuracy Road Object-Detection

no code implementations5 Jun 2016 Khalid Ashraf, Bichen Wu, Forrest N. Iandola, Mattthew W. Moskewicz, Kurt Keutzer

The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles.

Autonomous Vehicles object-detection +2

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