Search Results for author: Dequan Wang

Found 21 papers, 11 papers with code

On-target Adaptation

no code implementations2 Sep 2021 Dequan Wang, Shaoteng Liu, Sayna Ebrahimi, Evan Shelhamer, Trevor Darrell

Domain adaptation seeks to mitigate the shift between training on the \emph{source} domain and testing on the \emph{target} domain.

Domain Adaptation

BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud

no code implementations19 Jun 2020 Mong H. Ng, Kaahan Radia, Jianfei Chen, Dequan Wang, Ionel Gog, Joseph E. Gonzalez

Bird's-eye-view (BEV) is a powerful and widely adopted representation for road scenes that captures surrounding objects and their spatial locations, along with overall context in the scene.

Bird's-Eye View Semantic Segmentation Semantic 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 +3

Dynamic Scale Inference by Entropy Minimization

no code implementations8 Aug 2019 Dequan Wang, Evan Shelhamer, Bruno Olshausen, Trevor Darrell

Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field.

Semantic Segmentation

Monocular Plan View Networks for Autonomous Driving

no code implementations16 May 2019 Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.

3D Object Detection Autonomous Driving

Blurring the Line Between Structure and Learning to Optimize and Adapt Receptive Fields

no code implementations25 Apr 2019 Evan Shelhamer, Dequan Wang, Trevor Darrell

Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.

Semantic Segmentation

Objects as Points

70 code implementations16 Apr 2019 Xingyi Zhou, Dequan Wang, Philipp Krähenbühl

We model an object as a single point --- the center point of its bounding box.

Keypoint Detection Real-Time Object Detection

Joint Monocular 3D Vehicle Detection and Tracking

1 code implementation ICCV 2019 Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, Fisher Yu

The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.

3D Object Detection 3D Pose Estimation +4

Deep Object-Centric Policies for Autonomous Driving

no code implementations13 Nov 2018 Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways.

Autonomous Driving

VisDA: The Visual Domain Adaptation Challenge

1 code implementation18 Oct 2017 Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, Kate Saenko

We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains.

General Classification Image Classification +3

Deep Layer Aggregation

5 code implementations CVPR 2018 Fisher Yu, Dequan Wang, Evan Shelhamer, Trevor Darrell

We augment standard architectures with deeper aggregation to better fuse information across layers.

Iterative Object and Part Transfer for Fine-Grained Recognition

no code implementations29 Mar 2017 Zhiqiang Shen, Yu-Gang Jiang, Dequan Wang, xiangyang xue

On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

4 code implementations8 Dec 2016 Judy Hoffman, Dequan Wang, Fisher Yu, Trevor Darrell

In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.

Semantic Segmentation Synthetic-to-Real Translation

Learning to Point and Count

no code implementations8 Dec 2015 Jie Shao, Dequan Wang, xiangyang xue, Zheng Zhang

This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.

General Classification

Multiple Granularity Descriptors for Fine-Grained Categorization

no code implementations ICCV 2015 Dequan Wang, Zhiqiang Shen, Jie Shao, Wei zhang, xiangyang xue, Zheng Zhang

Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.

Weakly Supervised Semantic Segmentation for Social Images

no code implementations CVPR 2015 Wei Zhang, Sheng Zeng, Dequan Wang, xiangyang xue

Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.

Weakly-Supervised Semantic Segmentation

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