Search Results for author: Fisher Yu

Found 40 papers, 26 papers with code

TADA: Taxonomy Adaptive Domain Adaptation

no code implementations10 Sep 2021 Rui Gong, Martin Danelljan, Dengxin Dai, Wenguan Wang, Danda Pani Paudel, Ajad Chhatkuli, Fisher Yu, Luc van Gool

We extensively evaluate the effectiveness of our framework under different TADA settings: open taxonomy, coarse-to-fine taxonomy, and partially-overlapping taxonomy.

Contrastive Learning Domain Adaptation

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

1 code implementation18 Aug 2021 Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, Luc van Gool

Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the more challenging CARLA LeaderBoard.

Autonomous Driving Imitation Learning

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

no code implementations18 Aug 2021 Goutam Bhat, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.

Denoising Image Restoration +1

On the Practicality of Deterministic Epistemic Uncertainty

1 code implementation1 Jul 2021 Janis Postels, Mattia Segu, Tao Sun, Luc van Gool, Fisher Yu, Federico Tombari

A set of novel approaches for estimating epistemic uncertainty in deep neural networks with a single forward pass has recently emerged as a valid alternative to Bayesian Neural Networks.

Image Classification Semantic Segmentation

Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation

no code implementations22 Jun 2021 Lei Ke, Xia Li, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu

We propose Prototypical Cross-Attention Network (PCAN), capable of leveraging rich spatio-temporal information for online multiple object tracking and segmentation.

Multiple Object Track and Segmentation Video Instance Segmentation

Robust Object Detection via Instance-Level Temporal Cycle Confusion

1 code implementation16 Apr 2021 Xin Wang, Thomas E. Huang, Benlin Liu, Fisher Yu, Xiaolong Wang, Joseph E. Gonzalez, Trevor Darrell

Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.

Robust Object Detection

Warp Consistency for Unsupervised Learning of Dense Correspondences

1 code implementation7 Apr 2021 Prune Truong, Martin Danelljan, Fisher Yu, Luc van Gool

From our observations and empirical results, we design a general unsupervised objective employing two of the derived constraints.

Dense Pixel Correspondence Estimation

Monocular Quasi-Dense 3D Object Tracking

1 code implementation12 Mar 2021 Hou-Ning Hu, Yung-Hsu Yang, Tobias Fischer, Trevor Darrell, Fisher Yu, Min Sun

Experiments on our proposed simulation data and real-world benchmarks, including KITTI, nuScenes, and Waymo datasets, show that our tracking framework offers robust object association and tracking on urban-driving scenarios.

3D Object Tracking Autonomous Driving +2

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

3 code implementations28 Jan 2021 Wenguan Wang, Tianfei Zhou, Fisher Yu, Jifeng Dai, Ender Konukoglu, Luc van Gool

Inspired by the recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting.

Metric Learning Optical Character Recognition +2

Instance-Aware Predictive Navigation in Multi-Agent Environments

1 code implementation14 Jan 2021 Jinkun Cao, Xin Wang, Trevor Darrell, Fisher Yu

To decide the action at each step, we seek the action sequence that can lead to safe future states based on the prediction module outputs by repeatedly sampling likely action sequences.

Quasi-Dense Similarity Learning for Multiple Object Tracking

1 code implementation CVPR 2021 Jiangmiao Pang, Linlu Qiu, Xia Li, Haofeng Chen, Qi Li, Trevor Darrell, Fisher Yu

Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.

Contrastive Learning Metric Learning +3

Frustratingly Simple Few-Shot Object Detection

2 code implementations ICML 2020 Xin Wang, Thomas E. Huang, Trevor Darrell, Joseph E. Gonzalez, Fisher Yu

Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.

Few-Shot Object Detection Meta-Learning

Task-Aware Feature Generation for Zero-Shot Compositional Learning

1 code implementation11 Jun 2019 Xin Wang, Fisher Yu, Trevor Darrell, Joseph E. Gonzalez

In this work, we propose a task-aware feature generation (TFG) framework for compositional learning, which generates features of novel visual concepts by transferring knowledge from previously seen concepts.

Zero-Shot Learning

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

1 code implementation CVPR 2019 Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, Joseph E. Gonzalez

We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning.

Few-Shot Learning Zero-Shot Learning

Hierarchical Discrete Distribution Decomposition for Match Density Estimation

2 code implementations CVPR 2019 Zhichao Yin, Trevor Darrell, Fisher Yu

Explicit representations of the global match distributions of pixel-wise correspondences between pairs of images are desirable for uncertainty estimation and downstream applications.

Density Estimation Optical Flow Estimation +2

Few-shot Object Detection via Feature Reweighting

4 code implementations ICCV 2019 Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, Trevor Darrell

The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.

Few-Shot Learning Few-Shot Object Detection +1

Disentangling Propagation and Generation for Video Prediction

no code implementations ICCV 2019 Hang Gao, Huazhe Xu, Qi-Zhi Cai, Ruth Wang, Fisher Yu, Trevor Darrell

A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.

Predict Future Video Frames

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

Deep Mixture of Experts via Shallow Embedding

no code implementations5 Jun 2018 Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez

Larger networks generally have greater representational power at the cost of increased computational complexity.

Few-Shot Learning Zero-Shot Learning

PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

no code implementations CVPR 2018 Huiwen Chang, Jingwan Lu, Fisher Yu, Adam Finkelstein

This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo.

Style Transfer

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

1 code implementation CVPR 2020 Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.

2D Object Detection Autonomous Driving +8

Reinforcement Learning from Imperfect Demonstrations

no code implementations ICLR 2018 Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell

We propose a unified reinforcement learning algorithm, Normalized Actor-Critic (NAC), that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data.

SkipNet: Learning Dynamic Routing in Convolutional Networks

2 code implementations ECCV 2018 Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, Joseph E. Gonzalez

While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient.

Decision Making

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.

Interactive 3D Modeling with a Generative Adversarial Network

no code implementations16 Jun 2017 Jerry Liu, Fisher Yu, Thomas Funkhouser

This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface.

Minecraft

IDK Cascades: Fast Deep Learning by Learning not to Overthink

no code implementations3 Jun 2017 Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.

Dilated Residual Networks

2 code implementations CVPR 2017 Fisher Yu, Vladlen Koltun, Thomas Funkhouser

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible.

Classification General Classification +4

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

End-to-end Learning of Driving Models from Large-scale Video Datasets

2 code implementations CVPR 2017 Huazhe Xu, Yang Gao, Fisher Yu, Trevor Darrell

Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment.

Scene Segmentation

Scribbler: Controlling Deep Image Synthesis with Sketch and Color

1 code implementation CVPR 2017 Patsorn Sangkloy, Jingwan Lu, Chen Fang, Fisher Yu, James Hays

In this paper, we propose a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces.

Colorization Image Generation

Semantic Scene Completion from a Single Depth Image

3 code implementations CVPR 2017 Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation.

Multi-Scale Context Aggregation by Dilated Convolutions

9 code implementations23 Nov 2015 Fisher Yu, Vladlen Koltun

State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification.

Classification General Classification +1

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

3 code implementations10 Jun 2015 Fisher Yu, Ari Seff, yinda zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry.

Semantic Alignment of LiDAR Data at City Scale

no code implementations CVPR 2015 Fisher Yu, Jianxiong Xiao, Thomas Funkhouser

This paper describes an automatic algorithm for global alignment of LiDAR data collected with Google Street View cars in urban environments.

Pose Estimation Structure from Motion

3D ShapeNets: A Deep Representation for Volumetric Shapes

no code implementations CVPR 2015 Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao

Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.

3D Point Cloud Classification 3D Shape Representation +1

3D Reconstruction from Accidental Motion

no code implementations CVPR 2014 Fisher Yu, David Gallup

We have discovered that 3D reconstruction can be achieved from asingle still photographic capture due to accidental motions of thephotographer, even while attempting to hold the camera still.

3D Reconstruction Semantic Segmentation

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