Search Results for author: Martial Hebert

Found 68 papers, 15 papers with code

Generative Modeling for Multi-task Visual Learning

no code implementations25 Jun 2021 Zhipeng Bao, Martial Hebert, Yu-Xiong Wang

Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.

Multi-Task Learning

Learning to Track Object Position through Occlusion

no code implementations20 Jun 2021 Satyaki Chakraborty, Martial Hebert

Occlusion is one of the most significant challenges encountered by object detectors and trackers.

Object Detection

ZePHyR: Zero-shot Pose Hypothesis Rating

1 code implementation28 Apr 2021 Brian Okorn, Qiao Gu, Martial Hebert, David Held

We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation.

Motion Planning Pose Estimation

PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection

no code implementations17 Dec 2020 Xia Chen, Jianren Wang, David Held, Martial Hebert

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure.

Autonomous Driving Cloud Detection

Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method

1 code implementation15 Oct 2020 Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao

We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.

Model-based Reinforcement Learning Safe Reinforcement Learning

Few-Shot Learning with Intra-Class Knowledge Transfer

no code implementations22 Aug 2020 Vivek Roy, Yan Xu, Yu-Xiong Wang, Kris Kitani, Ruslan Salakhutdinov, Martial Hebert

Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models with the few-shot training samples as the seeds.

Few-Shot Learning Transfer Learning

Alpha Net: Adaptation with Composition in Classifier Space

no code implementations17 Aug 2020 Nadine Chang, Jayanth Koushik, Michael J. Tarr, Martial Hebert, Yu-Xiong Wang

Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples.

Transfer Learning

Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

no code implementations ICLR 2021 Zhipeng Bao, Yu-Xiong Wang, Martial Hebert

We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints.

Data Augmentation Multi-Task Learning +1

PanoNet: Real-time Panoptic Segmentation through Position-Sensitive Feature Embedding

no code implementations1 Aug 2020 Xia Chen, Jianren Wang, Martial Hebert

We propose a simple, fast, and flexible framework to generate simultaneously semantic and instance masks for panoptic segmentation.

Autonomous Driving Panoptic Segmentation

MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

no code implementations30 Jul 2020 Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao

Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.

Learning Orientation Distributions for Object Pose Estimation

1 code implementation2 Jul 2020 Brian Okorn, Mengyun Xu, Martial Hebert, David Held

Our first method, which regresses from deep learned features to an isotropic Bingham distribution, gives the best performance for orientation distribution estimation for non-symmetric objects.

Pose Estimation

Explainable Semantic Mapping for First Responders

no code implementations15 Oct 2019 Jean Oh, Martial Hebert, Hae-Gon Jeon, Xavier Perez, Chia Dai, Yeeho Song

One of the key challenges in the semantic mapping problem in postdisaster environments is how to analyze a large amount of data efficiently with minimal supervision.

Semantic Segmentation

Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

1 code implementation27 Jul 2019 Kashyap Chitta, Jose M. Alvarez, Martial Hebert

Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions.

Scene Parsing Semantic Segmentation

Growing a Brain: Fine-Tuning by Increasing Model Capacity

no code implementations CVPR 2017 Yu-Xiong Wang, Deva Ramanan, Martial Hebert

One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset.

Developmental Learning

Edge-Direct Visual Odometry

2 code implementations11 Jun 2019 Kevin Christensen, Martial Hebert

In contrast our method builds on direct visual odometry methods naturally with minimal added computation.

Edge Detection Visual Odometry

Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency

1 code implementation7 May 2019 Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, Martial Hebert

We demonstrate our ability to learn MVS without 3D supervision using a real dataset, and show that each component of our proposed robust loss results in a significant improvement.

Depth Estimation

A Study on Action Detection in the Wild

no code implementations29 Apr 2019 Yubo Zhang, Pavel Tokmakov, Martial Hebert, Cordelia Schmid

In this work we study the problem of action detection in a highly-imbalanced dataset.

Action Detection

Direct Fitting of Gaussian Mixture Models

1 code implementation11 Apr 2019 Leonid Keselman, Martial Hebert

Part of this work analyzes a general formulation for evaluating likelihood of geometric objects.

Learning Compositional Representations for Few-Shot Recognition

no code implementations ICCV 2019 Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert

One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.

Few-Shot Learning

A Structured Model For Action Detection

no code implementations CVPR 2019 Yubo Zhang, Pavel Tokmakov, Martial Hebert, Cordelia Schmid

A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand.

Action Detection Video Understanding

Iterative Transformer Network for 3D Point Cloud

1 code implementation27 Nov 2018 Wentao Yuan, David Held, Christoph Mertz, Martial Hebert

Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation.

General Classification

Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels

no code implementations8 Nov 2018 Kashyap Chitta, Jianwei Feng, Martial Hebert

With our design, the network progressively learns features specific to the target domain using annotation from only the source domain.

Semantic Segmentation Unsupervised Domain Adaptation

PCN: Point Completion Network

5 code implementations2 Aug 2018 Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, Martial Hebert

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications.

Point Cloud Completion

Deep Material-Aware Cross-Spectral Stereo Matching

no code implementations CVPR 2018 Tiancheng Zhi, Bernardo R. Pires, Martial Hebert, Srinivasa G. Narasimhan

Often, multiple cameras are used for cross-spectral imaging, thus requiring image alignment, or disparity estimation in a stereo setting.

Disparity Estimation Stereo Matching +1

Low-Shot Learning from Imaginary Data

no code implementations CVPR 2018 Yu-Xiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan

Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.

General Classification Meta-Learning

Learning by Asking Questions

no code implementations CVPR 2018 Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten

We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions.

Question Answering Visual Question Answering

Learning to Model the Tail

no code implementations NeurIPS 2017 Yu-Xiong Wang, Deva Ramanan, Martial Hebert

We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail.

Few-shot Regression Image Classification +1

Log-DenseNet: How to Sparsify a DenseNet

1 code implementation ICLR 2018 Hanzhang Hu, Debadeepta Dey, Allison Del Giorno, Martial Hebert, J. Andrew Bagnell

Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency.

Semantic Segmentation

Predictive-State Decoders: Encoding the Future into Recurrent Networks

no code implementations NeurIPS 2017 Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell

We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations.

Imitation Learning

Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing

no code implementations22 Aug 2017 Hanzhang Hu, Debadeepta Dey, Martial Hebert, J. Andrew Bagnell

Experimentally, the adaptive weights induce more competitive anytime predictions on multiple recognition data-sets and models than non-adaptive approaches including weighing all losses equally.

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

1 code implementation ICCV 2017 Debidatta Dwibedi, Ishan Misra, Martial Hebert

In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort.

Object Detection

From Red Wine to Red Tomato: Composition With Context

no code implementations CVPR 2017 Ishan Misra, Abhinav Gupta, Martial Hebert

In this paper, we present a simple method that respects contextuality in order to compose classifiers of known visual concepts.

The Pose Knows: Video Forecasting by Generating Pose Futures

no code implementations ICCV 2017 Jacob Walker, Kenneth Marino, Abhinav Gupta, Martial Hebert

First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space.

Video Prediction

Gradient Boosting on Stochastic Data Streams

no code implementations1 Mar 2017 Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell

To generalize from batch to online, we first introduce the definition of online weak learning edge with which for strongly convex and smooth loss functions, we present an algorithm, Streaming Gradient Boosting (SGB) with exponential shrinkage guarantees in the number of weak learners.

Contextual Visual Similarity

no code implementations8 Dec 2016 Xiaofang Wang, Kris M. Kitani, Martial Hebert

Given a query image, a second positive image and a third negative image, dissimilar to the first two images, we define a contextualized similarity search criteria.

Image Retrieval Image Similarity Search

General models for rational cameras and the case of two-slit projections

no code implementations CVPR 2017 Matthew Trager, Bernd Sturmfels, John Canny, Martial Hebert, Jean Ponce

The rational camera model recently introduced in [19] provides a general methodology for studying abstract nonlinear imaging systems and their multi-view geometry.

Structure from Motion

Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs

no code implementations NeurIPS 2016 Yu-Xiong Wang, Martial Hebert

Inspired by the transferability properties of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images.

Action Recognition General Classification +1

A Discriminative Framework for Anomaly Detection in Large Videos

no code implementations28 Sep 2016 Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering.

Anomaly Detection Density Estimation

Learning Transferable Policies for Monocular Reactive MAV Control

no code implementations1 Aug 2016 Shreyansh Daftry, J. Andrew Bagnell, Martial Hebert

The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning.

Introspective Perception: Learning to Predict Failures in Vision Systems

no code implementations28 Jul 2016 Shreyansh Daftry, Sam Zeng, J. Andrew Bagnell, Martial Hebert

As robots aspire for long-term autonomous operations in complex dynamic environments, the ability to reliably take mission-critical decisions in ambiguous situations becomes critical.

An Uncertain Future: Forecasting from Static Images using Variational Autoencoders

no code implementations25 Jun 2016 Jacob Walker, Carl Doersch, Abhinav Gupta, Martial Hebert

We show that our method is able to successfully predict events in a wide variety of scenes and can produce multiple different predictions when the future is ambiguous.

Consistency of Silhouettes and Their Duals

no code implementations CVPR 2016 Matthew Trager, Martial Hebert, Jean Ponce

Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape.

Object Recognition

Cross-stitch Networks for Multi-task Learning

2 code implementations CVPR 2016 Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert

In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning.

Multi-Task Learning

Learning to Extract Motion from Videos in Convolutional Neural Networks

no code implementations27 Jan 2016 Damien Teney, Martial Hebert

Our contributions on network design and rotation invariance offer insights nonspecific to motion estimation.

Motion Estimation Optical Flow Estimation

Predicting Multiple Structured Visual Interpretations

no code implementations ICCV 2015 Debadeepta Dey, Varun Ramakrishna, Martial Hebert, J. Andrew Bagnell

We present a simple approach for producing a small number of structured visual outputs which have high recall, for a variety of tasks including monocular pose estimation and semantic scene segmentation.

Pose Estimation Scene Segmentation

The Joint Image Handbook

no code implementations ICCV 2015 Matthew Trager, Martial Hebert, Jean Ponce

Given multiple perspective photographs, point correspondences form the "joint image", effectively a replica of three dimensional space distributed across its two-dimensional projections.

Inferring 3D Layout of Building Facades From a Single Image

no code implementations CVPR 2015 Jiyan Pan, Martial Hebert, Takeo Kanade

In this paper, we propose a novel algorithm that infers the 3D layout of building facades from a single 2D image of an urban scene.

Model Recommendation: Generating Object Detectors From Few Samples

no code implementations CVPR 2015 Yu-Xiong Wang, Martial Hebert

In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples.

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos

no code implementations21 May 2015 Ishan Misra, Abhinav Shrivastava, Martial Hebert

We present a semi-supervised approach that localizes multiple unknown object instances in long videos.

Object Detection

Dense Optical Flow Prediction from a Static Image

no code implementations ICCV 2015 Jacob Walker, Abhinav Gupta, Martial Hebert

Because our CNN model makes no assumptions about the underlying scene, it can predict future optical flow on a diverse set of scenarios.

motion prediction Optical Flow Estimation

Visual Chunking: A List Prediction Framework for Region-Based Object Detection

no code implementations27 Oct 2014 Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell

We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals.

Chunking Object Detection

Efficient Feature Group Sequencing for Anytime Linear Prediction

no code implementations19 Sep 2014 Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell, Martial Hebert

We theoretically guarantee that our algorithms achieve near-optimal linear predictions at each budget when a feature group is chosen.

Patch to the Future: Unsupervised Visual Prediction

no code implementations CVPR 2014 Jacob Walker, Abhinav Gupta, Martial Hebert

In this paper we present a conceptually simple but surprisingly powerful method for visual prediction which combines the effectiveness of mid-level visual elements with temporal modeling.

Trinocular Geometry Revisited

no code implementations CVPR 2014 Jean Ponce, Martial Hebert

When do the visual rays associated with triplets of point correspondences converge, that is, intersect in a common point?

Predicting Failures of Vision Systems

no code implementations CVPR 2014 Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, Devi Parikh

We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems – semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction – on individual input images.

Semantic Segmentation Zero-Shot Learning

SpeedMachines: Anytime Structured Prediction

no code implementations2 Dec 2013 Alexander Grubb, Daniel Munoz, J. Andrew Bagnell, Martial Hebert

Structured prediction plays a central role in machine learning applications from computational biology to computer vision.

General Classification Scene Understanding +1

An Integer Projected Fixed Point Method for Graph Matching and MAP Inference

no code implementations NeurIPS 2009 Marius Leordeanu, Martial Hebert, Rahul Sukthankar

When applied to MAP inference, the algorithm is a parallel extension of Iterated Conditional Modes (ICM) with climbing and convergence properties that make it a compelling alternative to the sequential ICM.

Graph Matching

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