Search Results for author: Li Fei-Fei

Found 161 papers, 55 papers with code

RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition

1 code implementation ECCV 2020 Linxi Fan, Shyamal Buch, Guanzhi Wang, Ryan Cao, Yuke Zhu, Juan Carlos Niebles, Li Fei-Fei

We analyze the suitability of our new primitive for video action recognition and explore several novel variations of our approach to enable stronger representational flexibility while maintaining an efficient design.

Action Recognition Video Recognition

MaskViT: Masked Visual Pre-Training for Video Prediction

no code implementations23 Jun 2022 Agrim Gupta, Stephen Tian, Yunzhi Zhang, Jiajun Wu, Roberto Martín-Martín, Li Fei-Fei

This work shows that we can create good video prediction models by pre-training transformers via masked visual modeling.

Video Prediction

BEHAVIOR in Habitat 2.0: Simulator-Independent Logical Task Description for Benchmarking Embodied AI Agents

no code implementations13 Jun 2022 Ziang Liu, Roberto Martín-Martín, Fei Xia, Jiajun Wu, Li Fei-Fei

Robots excel in performing repetitive and precision-sensitive tasks in controlled environments such as warehouses and factories, but have not been yet extended to embodied AI agents providing assistance in household tasks.

Natural Language Processing

PrivHAR: Recognizing Human Actions From Privacy-preserving Lens

no code implementations8 Jun 2022 Carlos Hinojosa, Miguel Marquez, Henry Arguello, Ehsan Adeli, Li Fei-Fei, Juan Carlos Niebles

The accelerated use of digital cameras prompts an increasing concern about privacy and security, particularly in applications such as action recognition.

Action Recognition Privacy Preserving

ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer

1 code implementation CVPR 2022 Ruohan Gao, Zilin Si, Yen-Yu Chang, Samuel Clarke, Jeannette Bohg, Li Fei-Fei, Wenzhen Yuan, Jiajun Wu

We present ObjectFolder 2. 0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances ObjectFolder 1. 0 in three aspects.

MetaMorph: Learning Universal Controllers with Transformers

1 code implementation ICLR 2022 Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei

Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning.

Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation

no code implementations9 Dec 2021 Josiah Wong, Albert Tung, Andrey Kurenkov, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Roberto Martín-Martín

Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstrations difficult, and on the learning side, policies trained on limited data can suffer from covariate shift when deployed.

Imitation Learning

Visual Intelligence through Human Interaction

no code implementations12 Nov 2021 Ranjay Krishna, Mitchell Gordon, Li Fei-Fei, Michael Bernstein

Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to describing pictures, answering questions about images, aiding robots maneuver around physical spaces and even generating novel visual content.

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

no code implementations21 Sep 2021 Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.

Model-based Reinforcement Learning reinforcement-learning

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

1 code implementation13 Aug 2021 Chen Wang, Claudia Pérez-D'Arpino, Danfei Xu, Li Fei-Fei, C. Karen Liu, Silvio Savarese

Our method co-optimizes a human policy and a robot policy in an interactive learning process: the human policy learns to generate diverse and plausible collaborative behaviors from demonstrations while the robot policy learns to assist by estimating the unobserved latent strategy of its human collaborator.

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments

no code implementations6 Aug 2021 Sanjana Srivastava, Chengshu Li, Michael Lingelbach, Roberto Martín-Martín, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.

iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

1 code implementation6 Aug 2021 Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, C. Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, Silvio Savarese

We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.

Imitation Learning

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

1 code implementation6 Aug 2021 Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, Roberto Martín-Martín

Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation.

Imitation Learning reinforcement-learning

Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning

no code implementations20 Jul 2021 Kaylee Burns, Christopher D. Manning, Li Fei-Fei

Although virtual agents are increasingly situated in environments where natural language is the most effective mode of interaction with humans, these exchanges are rarely used as an opportunity for learning.

Grounded language learning

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

1 code implementation ACL 2021 Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher D. Manning

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition.

Active Learning Object Recognition +3

Discovering Generalizable Skills via Automated Generation of Diverse Tasks

no code implementations26 Jun 2021 Kuan Fang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks.

Hierarchical Reinforcement Learning reinforcement-learning

Scalable Differential Privacy With Sparse Network Finetuning

no code implementations CVPR 2021 Zelun Luo, Daniel J. Wu, Ehsan Adeli, Li Fei-Fei

We propose a novel method for privacy-preserving training of deep neural networks leveraging public, out-domain data.

Privacy Preserving Transfer Learning

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

no code implementations17 Jun 2021 Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar

A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert.

Autonomous Driving Image Augmentation +1

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

2 code implementations CVPR 2022 Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.

Federated Learning

Metadata Normalization

1 code implementation CVPR 2021 Mandy Lu, Qingyu Zhao, Jiequan Zhang, Kilian M. Pohl, Li Fei-Fei, Juan Carlos Niebles, Ehsan Adeli

Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods.

A Study of Face Obfuscation in ImageNet

1 code implementation10 Mar 2021 Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky

In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark.

object-detection Object Detection +3

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

no code implementations CVPR 2021 Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn

Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.

Video Prediction

Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control

no code implementations28 Feb 2021 Chen Wang, Rui Wang, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Danfei Xu

Key to such capability is hand-eye coordination, a cognitive ability that enables humans to adaptively direct their movements at task-relevant objects and be invariant to the objects' absolute spatial location.

Imitation Learning

Embodied Intelligence via Learning and Evolution

1 code implementation3 Feb 2021 Agrim Gupta, Silvio Savarese, Surya Ganguli, Li Fei-Fei

However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, partially due to the substantial challenge of performing large-scale in silico experiments on evolution and learning.

Human-in-the-Loop Imitation Learning using Remote Teleoperation

no code implementations12 Dec 2020 Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese

We develop a simple and effective algorithm to train the policy iteratively on new data collected by the system that encourages the policy to learn how to traverse bottlenecks through the interventions.

Imitation Learning

Learning Multi-Arm Manipulation Through Collaborative Teleoperation

no code implementations12 Dec 2020 Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Li Fei-Fei, Silvio Savarese

To address these challenges, we present Multi-Arm RoboTurk (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms and collect demonstrations for multi-arm tasks.

Imitation Learning

Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

no code implementations5 Aug 2020 Pranav Khadpe, Ranjay Krishna, Li Fei-Fei, Jeffrey Hancock, Michael Bernstein

In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth.

Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity

no code implementations17 Jul 2020 Mandy Lu, Kathleen Poston, Adolf Pfefferbaum, Edith V. Sullivan, Li Fei-Fei, Kilian M. Pohl, Juan Carlos Niebles, Ehsan Adeli

This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity.

Adaptive Procedural Task Generation for Hard-Exploration Problems

no code implementations ICLR 2021 Kuan Fang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

To enable curriculum learning in the absence of a direct indicator of learning progress, we propose to train the task generator by balancing the agent's performance in the generated tasks and the similarity to the target tasks.

Learning Physical Graph Representations from Visual Scenes

1 code implementation NeurIPS 2020 Daniel M. Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins

To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.

Object Categorization Scene Segmentation

Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations

no code implementations13 Mar 2020 Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei

In the second stage of GTI, we collect a small set of rollouts from the unconditioned stochastic policy of the first stage, and train a goal-directed agent to generalize to novel start and goal configurations.

Imitation Learning

Action Genome: Actions as Composition of Spatio-temporal Scene Graphs

1 code implementation15 Dec 2019 Jingwei Ji, Ranjay Krishna, Li Fei-Fei, Juan Carlos Niebles

Next, by decomposing and learning the temporal changes in visual relationships that result in an action, we demonstrate the utility of a hierarchical event decomposition by enabling few-shot action recognition, achieving 42. 7% mAP using as few as 10 examples.

Few Shot Action Recognition

Deep Bayesian Active Learning for Multiple Correct Outputs

no code implementations2 Dec 2019 Khaled Jedoui, Ranjay Krishna, Michael Bernstein, Li Fei-Fei

The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs.

Active Learning Answer Generation +5

Motion Reasoning for Goal-Based Imitation Learning

no code implementations13 Nov 2019 De-An Huang, Yu-Wei Chao, Chris Paxton, Xinke Deng, Li Fei-Fei, Juan Carlos Niebles, Animesh Garg, Dieter Fox

We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.

Imitation Learning Motion Planning

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

no code implementations13 Nov 2019 Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox

For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task.

Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments

1 code implementation30 Oct 2019 Fei Xia, William B. Shen, Chengshu Li, Priya Kasimbeg, Micael Tchapmi, Alexander Toshev, Li Fei-Fei, Roberto Martín-Martín, Silvio Savarese

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation: robot navigation strategies where physical interaction with objects is allowed and even encouraged to accomplish a task.

Robot Navigation

Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

no code implementations29 Oct 2019 Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei

The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal.

Variational Inference

KETO: Learning Keypoint Representations for Tool Manipulation

no code implementations26 Oct 2019 Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei, Silvio Savarese

For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation.

Robotics

Representation Learning with Statistical Independence to Mitigate Bias

2 code implementations8 Oct 2019 Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan, Li Fei-Fei, Juan Carlos Niebles, Kilian M. Pohl

Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years.

Face Recognition Representation Learning

Causal Induction from Visual Observations for Goal Directed Tasks

1 code implementation3 Oct 2019 Suraj Nair, Yuke Zhu, Silvio Savarese, Li Fei-Fei

Causal reasoning has been an indispensable capability for humans and other intelligent animals to interact with the physical world.

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

1 code implementation28 Sep 2019 Yunbo Wang, Bo Liu, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty.

Continuous Control

Regression Planning Networks

1 code implementation NeurIPS 2019 Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

Recent learning-to-plan methods have shown promising results on planning directly from observation space.

SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning

no code implementations27 Sep 2019 Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, Li Fei-Fei

We present an overview of SURREAL-System, a reproducible, flexible, and scalable framework for distributed reinforcement learning (RL).

OpenAI Gym reinforcement-learning

Stochastic Neural Physics Predictor

no code implementations25 Sep 2019 Piotr Tatarczyk, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins, Nils Thuerey

Recently, neural-network based forward dynamics models have been proposed that attempt to learn the dynamics of physical systems in a deterministic way.

Situational Fusion of Visual Representation for Visual Navigation

no code implementations ICCV 2019 Bokui Shen, Danfei Xu, Yuke Zhu, Leonidas J. Guibas, Li Fei-Fei, Silvio Savarese

A complex visual navigation task puts an agent in different situations which call for a diverse range of visual perception abilities.

Visual Navigation

Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

no code implementations16 Aug 2019 De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles

The key technical challenge is that the symbol grounding is prone to error with limited training data and leads to subsequent symbolic planning failures.

Imitation Learning

Procedure Planning in Instructional Videos

no code implementations ECCV 2020 Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, Juan Carlos Niebles

In this paper, we study the problem of procedure planning in instructional videos, which can be seen as a step towards enabling autonomous agents to plan for complex tasks in everyday settings such as cooking.

Eidetic 3D LSTM: A Model for Video Prediction and Beyond

2 code implementations ICLR 2019 Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Activity Recognition Video Prediction

Scene Graph Prediction with Limited Labels

1 code implementation ICCV 2019 Vincent S. Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Re, Li Fei-Fei

All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.

Knowledge Base Completion Question Answering +2

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

no code implementations NeurIPS 2019 Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, Michael S. Bernstein

We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time.

Image Generation Unconditional Image Generation

Information Maximizing Visual Question Generation

no code implementations CVPR 2019 Ranjay Krishna, Michael Bernstein, Li Fei-Fei

We build a model that maximizes mutual information between the image, the expected answer and the generated question.

Question Generation

Composing Text and Image for Image Retrieval - An Empirical Odyssey

4 code implementations CVPR 2019 Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image.

Image Retrieval Image Retrieval with Multi-Modal Query

Vision-Based Gait Analysis for Senior Care

no code implementations1 Dec 2018 David Xue, Anin Sayana, Evan Darke, Kelly Shen, Jun-Ting Hsieh, Zelun Luo, Li-Jia Li, N. Lance Downing, Arnold Milstein, Li Fei-Fei

As the senior population rapidly increases, it is challenging yet crucial to provide effective long-term care for seniors who live at home or in senior care facilities.

Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

no code implementations25 Nov 2018 Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein, Li Fei-Fei

Computer-vision hospital systems can greatly assist healthcare workers and improve medical facility treatment, but often face patient resistance due to the perceived intrusiveness and violation of privacy associated with visual surveillance.

Action Recognition Privacy Preserving +1

Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference

no code implementations25 Nov 2018 Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, Li Fei-Fei

Homomorphic encryption enables arbitrary computation over data while it remains encrypted.

Cryptography and Security

Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions

no code implementations21 Nov 2018 Albert Haque, Michelle Guo, Adam S. Miner, Li Fei-Fei

This technology could be deployed to cell phones worldwide and facilitate low-cost universal access to mental health care.

Natural Language Processing Speech Recognition

RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

no code implementations7 Nov 2018 Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, Silvio Savarese, Li Fei-Fei

Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification.

Imitation Learning

Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

no code implementations ECCV 2018 Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles

A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions.

Video Retrieval

Neural Graph Matching Networks for Fewshot 3D Action Recognition

no code implementations ECCV 2018 Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei

We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples.

Few-Shot Learning Graph Matching +1

HiDDeN: Hiding Data With Deep Networks

6 code implementations ECCV 2018 Jiren Zhu, Russell Kaplan, Justin Johnson, Li Fei-Fei

We show that these encodings are competitive with existing data hiding algorithms, and further that they can be made robust to noise: our models learn to reconstruct hidden information in an encoded image despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and JPEG compression.

Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration

no code implementations CVPR 2019 De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles

We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model.

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?

no code implementations ICML 2018 Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei

One of the most widely used optimization methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD).

Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision

no code implementations25 Jun 2018 Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese

We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering.

Flexible Neural Representation for Physics Prediction

no code implementations NeurIPS 2018 Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins

Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail.

Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

no code implementations CVPR 2018 De-An Huang, Shyamal Buch, Lucio Dery, Animesh Garg, Li Fei-Fei, Juan Carlos Niebles

In this work, we propose to tackle this new task with a weakly-supervised framework for reference-aware visual grounding in instructional videos, where only the temporal alignment between the transcription and the video segment are available for supervision.

Multiple Instance Learning Visual Grounding

Image Generation from Scene Graphs

4 code implementations CVPR 2018 Justin Johnson, Agrim Gupta, Li Fei-Fei

To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships.

Image Generation from Scene Graphs Layout-to-Image Generation

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

7 code implementations CVPR 2018 Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, Alexandre Alahi

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments.

Motion Forecasting Multi-future Trajectory Prediction +2

Iterative Visual Reasoning Beyond Convolutions

no code implementations CVPR 2018 Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta

The framework consists of two core modules: a local module that uses spatial memory to store previous beliefs with parallel updates; and a global graph-reasoning module.

Visual Reasoning

Referring Relationships

2 code implementations CVPR 2018 Ranjay Krishna, Ines Chami, Michael Bernstein, Li Fei-Fei

We formulate the cyclic condition between the entities in a relationship by modelling predicates that connect the entities as shifts in attention from one entity to another.

Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

no code implementations24 Feb 2018 Amy Jin, Serena Yeung, Jeffrey Jopling, Jonathan Krause, Dan Azagury, Arnold Milstein, Li Fei-Fei

We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection.

Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation

no code implementations21 Feb 2018 Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins

Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks.

motion prediction

Learning to Play with Intrinsically-Motivated Self-Aware Agents

no code implementations21 Feb 2018 Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins

We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.

motion prediction

Progressive Neural Architecture Search

14 code implementations ECCV 2018 Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.

General Classification Image Classification +2

Graph Distillation for Action Detection with Privileged Modalities

1 code implementation ECCV 2018 Zelun Luo, Jun-Ting Hsieh, Lu Jiang, Juan Carlos Niebles, Li Fei-Fei

We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available.

Action Classification Action Detection +1

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

1 code implementation4 Oct 2017 Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, Silvio Savarese

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction.

Few-Shot Learning Program induction

Scalable Annotation of Fine-Grained Categories Without Experts

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei

We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts.

Fine-grained Recognition in the Wild: A Multi-Task Domain Adaptation Approach

no code implementations ICCV 2017 Timnit Gebru, Judy Hoffman, Li Fei-Fei

While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild.

Domain Adaptation Object Recognition

Fine-Grained Car Detection for Visual Census Estimation

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Li Fei-Fei

In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data.

Tackling Over-pruning in Variational Autoencoders

no code implementations9 Jun 2017 Serena Yeung, Anitha Kannan, Yann Dauphin, Li Fei-Fei

The so-called epitomes of this model are groups of mutually exclusive latent factors that compete to explain the data.

Learning to Learn from Noisy Web Videos

no code implementations CVPR 2017 Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei

Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.

Action Recognition Q-Learning

Visual Semantic Planning using Deep Successor Representations

no code implementations ICCV 2017 Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi

A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world.

Imitation Learning reinforcement-learning

Inferring and Executing Programs for Visual Reasoning

5 code implementations ICCV 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.

Visual Question Answering Visual Reasoning

Dense-Captioning Events in Videos

4 code implementations ICCV 2017 Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, Juan Carlos Niebles

We also introduce ActivityNet Captions, a large-scale benchmark for dense-captioning events.

Video Retrieval

Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos

no code implementations CVPR 2017 De-An Huang, Joseph J. Lim, Li Fei-Fei, Juan Carlos Niebles

We propose an unsupervised method for reference resolution in instructional videos, where the goal is to temporally link an entity (e. g., "dressing") to the action (e. g., "mix yogurt") that produced it.

Referring Expression

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

no code implementations22 Feb 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors.

Scene Graph Generation by Iterative Message Passing

3 code implementations CVPR 2017 Danfei Xu, Yuke Zhu, Christopher B. Choy, Li Fei-Fei

In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image.

Graph Generation Scene Graph Generation

Unsupervised Learning of Long-Term Motion Dynamics for Videos

no code implementations CVPR 2017 Zelun Luo, Boya Peng, De-An Huang, Alexandre Alahi, Li Fei-Fei

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos.

Representation Learning

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

4 code implementations CVPR 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.

Question Answering Visual Question Answering +1

Recurrent Attention Models for Depth-Based Person Identification

no code implementations CVPR 2016 Albert Haque, Alexandre Alahi, Li Fei-Fei

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark.

Person Identification reinforcement-learning

A Hierarchical Approach for Generating Descriptive Image Paragraphs

3 code implementations CVPR 2017 Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei

Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail.

Image Captioning Image Paragraph Captioning

Crowdsourcing in Computer Vision

no code implementations7 Nov 2016 Adriana Kovashka, Olga Russakovsky, Li Fei-Fei, Kristen Grauman

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts.

Object Recognition

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

2 code implementations16 Sep 2016 Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi

To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine.

3D Reconstruction Feature Engineering +2

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

no code implementations15 Sep 2016 Kenji Hata, Ranjay Krishna, Li Fei-Fei, Michael S. Bernstein

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets.

Visual Relationship Detection with Language Priors

no code implementations31 Jul 2016 Cewu Lu, Ranjay Krishna, Michael Bernstein, Li Fei-Fei

We improve on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship.

Content-Based Image Retrieval Visual Relationship Detection +1

Connectionist Temporal Modeling for Weakly Supervised Action Labeling

no code implementations28 Jul 2016 De-An Huang, Li Fei-Fei, Juan Carlos Niebles

We propose a weakly-supervised framework for action labeling in video, where only the order of occurring actions is required during training time.

General Classification

Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions

no code implementations7 Jun 2016 Marius Cătălin Iordan, Armand Joulin, Diane M. Beck, Li Fei-Fei

Our method outperforms the two most commonly used alternatives (anatomical landmark-based AFNI alignment and cortical convexity-based FreeSurfer alignment) in overlap between predicted region and functionally-defined LOC.

Embracing Error to Enable Rapid Crowdsourcing

no code implementations14 Feb 2016 Ranjay Krishna, Kenji Hata, Stephanie Chen, Joshua Kravitz, David A. Shamma, Li Fei-Fei, Michael S. Bernstein

Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data.

General Classification Sentiment Analysis +2

RGB-W: When Vision Meets Wireless

no code implementations ICCV 2015 Alexandre Alahi, Albert Haque, Li Fei-Fei

Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional "quasi-free" modality, namely, the wireless signal (e. g., wifi or Bluetooth) emitted by individuals' cell phones, referred to as RGB-W.

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

1 code implementation CVPR 2016 Justin Johnson, Andrej Karpathy, Li Fei-Fei

We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language.

Image Captioning Language Modelling +2

End-to-end Learning of Action Detection from Frame Glimpses in Videos

no code implementations CVPR 2016 Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei

In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions.

Ranked #9 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.2 metric)

Action Detection

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

1 code implementation20 Nov 2015 Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.

Active Learning

Detecting events and key actors in multi-person videos

no code implementations CVPR 2016 Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei

In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event.

Event Detection General Classification

Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries

no code implementations20 Jul 2015 Yuke Zhu, Ce Zhang, Christopher Ré, Li Fei-Fei

The complexity of the visual world creates significant challenges for comprehensive visual understanding.

What's the Point: Semantic Segmentation with Point Supervision

1 code implementation6 Jun 2015 Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei

The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost.

Semantic Segmentation

Visualizing and Understanding Recurrent Networks

3 code implementations5 Jun 2015 Andrej Karpathy, Justin Johnson, Li Fei-Fei

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data.

Best of Both Worlds: Human-Machine Collaboration for Object Annotation

no code implementations CVPR 2015 Olga Russakovsky, Li-Jia Li, Li Fei-Fei

This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images.

object-detection Object Detection

Image Retrieval Using Scene Graphs

no code implementations CVPR 2015 Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, Li Fei-Fei

We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.

Image Retrieval Object Localization

Fine-Grained Recognition Without Part Annotations

no code implementations CVPR 2015 Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories.

Improving Image Classification with Location Context

no code implementations ICCV 2015 Kevin Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir Bourdev

With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them.

Classification General Classification +1

Visual Noise from Natural Scene Statistics Reveals Human Scene Category Representations

no code implementations19 Nov 2014 Michelle R. Greene, Abraham P. Botros, Diane M. Beck, Li Fei-Fei

In this work, we visualize observers' internal representations of a visual scene category (street) using an experiment in which the observer views the naturalistic visual noise and collaborates with the algorithm to externalize his internal representation.

Affordances Provide a Fundamental Categorization Principle for Visual Scenes

no code implementations19 Nov 2014 Michelle R. Greene, Christopher Baldassano, Andre Esteva, Diane M. Beck, Li Fei-Fei

Traditional models of visual perception posit that scene categorization is achieved through the recognition of a scene's objects, yet these models cannot account for the mounting evidence that human observers are relatively insensitive to the local details in an image.

ImageNet Large Scale Visual Recognition Challenge

9 code implementations1 Sep 2014 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.

General Classification Image Classification +3

Large-Scale Video Classification with Convolutional Neural Networks

no code implementations 2014 IEEE Conference on Computer Vision and Pattern Recognition 2014 Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei

We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63. 3% up from 43. 9%).

Action Recognition Classification +2

VideoSET: Video Summary Evaluation through Text

no code implementations23 Jun 2014 Serena Yeung, Alireza Fathi, Li Fei-Fei

In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video.

Deep Fragment Embeddings for Bidirectional Image Sentence Mapping

no code implementations NeurIPS 2014 Andrej Karpathy, Armand Joulin, Li Fei-Fei

We introduce a model for bidirectional retrieval of images and sentences through a multi-modal embedding of visual and natural language data.

Referring Expression Comprehension

Socially-aware Large-scale Crowd Forecasting

no code implementations CVPR 2014 Alexandre Alahi, Vignesh Ramanathan, Li Fei-Fei

In crowded spaces such as city centers or train stations, human mobility looks complex, but is often influenced only by a few causes.

Fine-Grained Crowdsourcing for Fine-Grained Recognition

no code implementations CVPR 2013 Jia Deng, Jonathan Krause, Li Fei-Fei

In this work, we include humans in the loop to help computers select discriminative features.

feature selection

Discriminative Segment Annotation in Weakly Labeled Video

no code implementations CVPR 2013 Kevin Tang, Rahul Sukthankar, Jay Yagnik, Li Fei-Fei

Second, we ensure that CRANE is robust to label noise, both in terms of tagged videos that fail to contain the concept as well as occasional negative videos that do.

Large Margin Learning of Upstream Scene Understanding Models

no code implementations NeurIPS 2010 Jun Zhu, Li-Jia Li, Li Fei-Fei, Eric P. Xing

This paper presents a joint max-margin and max-likelihood learning method for upstream scene understanding models, in which latent topic discovery and prediction model estimation are closely coupled and well-balanced.

General Classification Scene Classification +2

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

no code implementations NeurIPS 2010 Li-Jia Li, Hao Su, Li Fei-Fei, Eric P. Xing

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings.

General Classification Object Recognition +1

Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis

no code implementations NeurIPS 2009 Barry Chai, Dirk Walther, Diane Beck, Li Fei-Fei

In this study, we present a method for estimating the mutual information for a localized pattern of fMRI data.

Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions

no code implementations NeurIPS 2009 Bangpeng Yao, Dirk Walther, Diane Beck, Li Fei-Fei

In this paper, we propose to model such connections in an Hidden Conditional Random Field (HCRF) framework, where the classifier of one region of interest (ROI) makes predictions based on not only its voxels but also the classifier predictions from ROIs that it connects to.

Classification General Classification

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