Search Results for author: Song-Chun Zhu

Found 163 papers, 40 papers with code

Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions

no code implementations EMNLP 2021 Arjun Akula, Spandana Gella, Keze Wang, Song-Chun Zhu, Siva Reddy

Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.

CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization

no code implementations EMNLP 2021 Arjun Akula, Soravit Changpinyo, Boqing Gong, Piyush Sharma, Song-Chun Zhu, Radu Soricut

One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role.

Question-Answer-Generation Question Answering +1

Learning from the Tangram to Solve Mini Visual Tasks

1 code implementation12 Dec 2021 Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu

Current pre-training methods in computer vision focus on natural images in the daily-life context.

Few-Shot Learning

ValueNet: A New Dataset for Human Value Driven Dialogue System

no code implementations12 Dec 2021 Liang Qiu, Yizhou Zhao, Jinchao Li, Pan Lu, Baolin Peng, Jianfeng Gao, Song-Chun Zhu

To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems.

Dialogue Generation Emotion Recognition +1

Robust Visual Reasoning via Language Guided Neural Module Networks

no code implementations NeurIPS 2021 Arjun Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu

Neural module networks (NMN) are a popular approach for solving multi-modal tasks such as visual question answering (VQA) and visual referring expression recognition (REF).

Question Answering Visual Question Answering +1

Emergent Graphical Conventions in a Visual Communication Game

no code implementations28 Nov 2021 Shuwen Qiu, Sirui Xie, Lifeng Fan, Tao Gao, Song-Chun Zhu, Yixin Zhu

While recent studies of emergent communication primarily focus on symbolic languages, their settings overlook the graphical sketches existing in human communication; they do not account for the evolution process through which symbolic sign systems emerge in the trade-off between iconicity and symbolicity.

Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning

no code implementations25 Nov 2021 Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu

Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization.

Systematic Generalization

Unsupervised Foreground Extraction via Deep Region Competition

1 code implementation NeurIPS 2021 Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background.

Semantic Segmentation

IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning

1 code implementation25 Oct 2021 Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei zhang, Zhou Yu, Xiaodan Liang, Song-Chun Zhu

Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset.

Object Recognition Question Answering +1

Iterative Teacher-Aware Learning

no code implementations NeurIPS 2021 Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu

Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works.

Emergence of Theory of Mind Collaboration in Multiagent Systems

no code implementations30 Sep 2021 Luyao Yuan, Zipeng Fu, Linqi Zhou, Kexin Yang, Song-Chun Zhu

Currently, in the study of multiagent systems, the intentions of agents are usually ignored.

Decision Making

YouRefIt: Embodied Reference Understanding with Language and Gesture

no code implementations ICCV 2021 Yixin Chen, Qing Li, Deqian Kong, Yik Lun Kei, Song-Chun Zhu, Tao Gao, Yixin Zhu, Siyuan Huang

To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.

Human robot interaction

CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models

1 code implementation3 Sep 2021 Arjun R. Akula, Keze Wang, Changsong Liu, Sari Saba-Sadiya, Hongjing Lu, Sinisa Todorovic, Joyce Chai, Song-Chun Zhu

More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user.

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds

1 code implementation ICCV 2021 Siyuan Huang, Yichen Xie, Song-Chun Zhu, Yixin Zhu

To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.

3D Object Detection 3D Point Cloud Classification +5

STAR: Sparse Transformer-based Action Recognition

1 code implementation15 Jul 2021 Feng Shi, Chonghan Lee, Liang Qiu, Yizhou Zhao, Tianyi Shen, Shivran Muralidhar, Tian Han, Song-Chun Zhu, Vijaykrishnan Narayanan

The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.

Action Recognition

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

no code implementations ACL 2021 Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly.

Dialog Relation Extraction

VersaGNN: a Versatile accelerator for Graph neural networks

no code implementations4 May 2021 Feng Shi, Ahren Yiqiao Jin, Song-Chun Zhu

As GNNs operate on non-Euclidean data, their irregular data access patterns cause considerable computational costs and overhead on conventional architectures, such as GPU and CPU.

Graph Generation Graph Matching +1

Learning Triadic Belief Dynamics in Nonverbal Communication from Videos

1 code implementation CVPR 2021 Lifeng Fan, Shuwen Qiu, Zilong Zheng, Tao Gao, Song-Chun Zhu, Yixin Zhu

By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents.

Scene Understanding

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis

1 code implementation CVPR 2021 Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu

Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose.

Novel View Synthesis

Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments

1 code implementation30 Mar 2021 Muzhi Han, Zeyu Zhang, Ziyuan Jiao, Xu Xie, Yixin Zhu, Song-Chun Zhu, Hangxin Liu

In this paper, we rethink the problem of scene reconstruction from an embodied agent's perspective: While the classic view focuses on the reconstruction accuracy, our new perspective emphasizes the underlying functions and constraints such that the reconstructed scenes provide \em{actionable} information for simulating \em{interactions} with agents.

Common Sense Reasoning

Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance

1 code implementation26 Mar 2021 Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

Predicting agents' future trajectories plays a crucial role in modern AI systems, yet it is challenging due to intricate interactions exhibited in multi-agent systems, especially when it comes to collision avoidance.

Trajectory Prediction

Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution

no code implementations CVPR 2021 Chi Zhang, Baoxiong Jia, Song-Chun Zhu, Yixin Zhu

To fill in this gap, we propose a neuro-symbolic Probabilistic Abduction and Execution (PrAE) learner; central to the PrAE learner is the process of probabilistic abduction and execution on a probabilistic scene representation, akin to the mental manipulation of objects.

ACRE: Abstract Causal REasoning Beyond Covariation

no code implementations CVPR 2021 Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, Yixin Zhu

Causal induction, i. e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data.

Causal Discovery Visual Reasoning

Towards Socially Intelligent Agents with Mental State Transition and Human Utility

no code implementations12 Mar 2021 Liang Qiu, Yizhou Zhao, Yuan Liang, Pan Lu, Weiyan Shi, Zhou Yu, Song-Chun Zhu

Building a socially intelligent agent involves many challenges, one of which is to track the agent's mental state transition and teach the agent to make rational decisions guided by its utility like a human.

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation

no code implementations7 Mar 2021 Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu

This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model.

Translation Unsupervised Image-To-Image Translation

Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

no code implementations6 Mar 2021 Xiaofeng Gao, Luyao Yuan, Tianmin Shu, Hongjing Lu, Song-Chun Zhu

Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth.

Motion Planning

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics

no code implementations2 Mar 2021 Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

Inspired by humans' remarkable ability to master arithmetic and generalize to unseen problems, we present a new dataset, HINT, to study machines' capability of learning generalizable concepts at three different levels: perception, syntax, and semantics.

Program Synthesis Systematic Generalization

HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving

no code implementations22 Feb 2021 Sirui Xie, Xiaojian Ma, Peiyu Yu, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances.

Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning

no code implementations1 Jan 2021 Chi Zhang, Sirui Xie, Baoxiong Jia, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.

Systematic Generalization

Transformers satisfy

no code implementations1 Jan 2021 Feng Shi, Chen Li, Shijie Bian, Yiqiao Jin, Ziheng Xu, Tian Han, Song-Chun Zhu

The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints.

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

no code implementations27 Dec 2020 Yining Hong, Qing Li, Ran Gong, Daniel Ciao, Siyuan Huang, Song-Chun Zhu

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability.

Mathematical Reasoning

Learning by Fixing: Solving Math Word Problems with Weak Supervision

1 code implementation19 Dec 2020 Yining Hong, Qing Li, Daniel Ciao, Siyuan Huang, Song-Chun Zhu

To generate more diverse solutions, \textit{tree regularization} is applied to guide the efficient shrinkage and exploration of the solution space, and a \textit{memory buffer} is designed to track and save the discovered various fixes for each problem.

 Ranked #1 on Math Word Problem Solving on Math23K (weakly-supervised metric)

Weighted Entropy Modification for Soft Actor-Critic

no code implementations18 Nov 2020 Yizhou Zhao, Song-Chun Zhu

We generalize the existing principle of the maximum Shannon entropy in reinforcement learning (RL) to weighted entropy by characterizing the state-action pairs with some qualitative weights, which can be connected with prior knowledge, experience replay, and evolution process of the policy.

Generalized Inverse Planning: Learning Lifted non-Markovian Utility for Generalizable Task Representation

no code implementations12 Nov 2020 Sirui Xie, Feng Gao, Song-Chun Zhu

Seeing that the proposed generalization problem has not been widely studied yet, we carefully define an evaluation protocol, with which we illustrate the effectiveness of MEIP on two proof-of-concept domains and one challenging task: learning to fold from demonstrations.

A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras

no code implementations28 Sep 2020 Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu

The grid cells in the mammalian medial entorhinal cortex exhibit striking hexagon firing patterns when the agent navigates in the open field.

Structured Attention for Unsupervised Dialogue Structure Induction

1 code implementation EMNLP 2020 Liang Qiu, Yizhou Zhao, Weiyan Shi, Yuan Liang, Feng Shi, Tao Yuan, Zhou Yu, Song-Chun Zhu

Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics.

Sentence Embeddings

LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities

no code implementations ECCV 2020 Baoxiong Jia, Yixin Chen, Siyuan Huang, Yixin Zhu, Song-Chun Zhu

Understanding and interpreting human actions is a long-standing challenge and a critical indicator of perception in artificial intelligence.

Action Recognition Action Understanding +1

Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks

no code implementations24 Jul 2020 Xiaofeng Gao, Ran Gong, Yizhou Zhao, Shu Wang, Tianmin Shu, Song-Chun Zhu

Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.

Bayesian Inference

On Path Integration of Grid Cells: Group Representation and Isotropic Scaling

1 code implementation NeurIPS 2021 Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu

In this paper, we conduct theoretical analysis of a general representation model of path integration by grid cells, where the 2D self-position is encoded as a higher dimensional vector, and the 2D self-motion is represented by a general transformation of the vector.

Dimensionality Reduction

Learning Latent Space Energy-Based Prior Model

1 code implementation NeurIPS 2020 Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

Due to the low dimensionality of the latent space and the expressiveness of the top-down network, a simple EBM in latent space can capture regularities in the data effectively, and MCMC sampling in latent space is efficient and mixes well.

Anomaly Detection Text Generation

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC

no code implementations12 Jun 2020 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

We show that the model has a particularly simple form in the space of the latent variables of the flow-based model, and MCMC sampling of the EBM in the latent space, which is a simple special case of neural transport MCMC, mixes well and traverses modes in the data space.

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

1 code implementation ICML 2020 Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu

In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently.

Question Answering Visual Question Answering

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

no code implementations CVPR 2020 Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu

This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and the latent energy-based model (EBM).

Anomaly Detection

Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models

1 code implementation ICLR 2021 Mitch Hill, Jonathan Mitchell, Song-Chun Zhu

Our contributions are 1) an improved method for training EBM's with realistic long-run MCMC samples, 2) an Expectation-Over-Transformation (EOT) defense that resolves theoretical ambiguities for stochastic defenses and from which the EOT attack naturally follows, and 3) state-of-the-art adversarial defense for naturally-trained classifiers and competitive defense compared to adversarially-trained classifiers on Cifar-10, SVHN, and Cifar-100.

Adversarial Defense Robust classification

Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions

1 code implementation ACL 2020 Arjun R. Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, Siva Reddy

To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn't.

Contrastive Learning Multi-Task Learning +1

Congestion-aware Evacuation Routing using Augmented Reality Devices

no code implementations25 Apr 2020 Zeyu Zhang, Hangxin Liu, Ziyuan Jiao, Yixin Zhu, Song-Chun Zhu

We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees' locations.

Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs

no code implementations25 Apr 2020 Tao Yuan, Hangxin Liu, Lifeng Fan, Zilong Zheng, Tao Gao, Yixin Zhu, Song-Chun Zhu

Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs.

Object Tracking

Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning

2 code implementations25 Apr 2020 Wenhe Zhang, Chi Zhang, Yixin Zhu, Song-Chun Zhu

To endow such a crucial cognitive ability to machine intelligence, we propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG).

Relational Reasoning Visual Reasoning

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

no code implementations20 Apr 2020 Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu

We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.

Common Sense Reasoning Small Data Image Classification

Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification

1 code implementation CVPR 2021 Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network.

General Classification Point Cloud Classification +2

PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points

no code implementations NeurIPS 2019 Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu

Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus requiring appropriate prior knowledge and intermediate representations as constraints to reduce the uncertainties and improve the consistencies between the 2D image plane and the 3D world coordinate.

Monocular 3D Object Detection

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

no code implementations ECCV 2020 Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.

Learning Perceptual Inference by Contrasting

1 code implementation NeurIPS 2019 Chi Zhang, Baoxiong Jia, Feng Gao, Yixin Zhu, Hongjing Lu, Song-Chun Zhu

"Thinking in pictures," [1] i. e., spatial-temporal reasoning, effortless and instantaneous for humans, is believed to be a significant ability to perform logical induction and a crucial factor in the intellectual history of technology development.

Representation Learning: A Statistical Perspective

no code implementations26 Nov 2019 Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

Learning representations of data is an important problem in statistics and machine learning.

Representation Learning

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

no code implementations26 Nov 2019 Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels.

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

no code implementations25 Nov 2019 Mark Edmonds, Xiaojian Ma, Siyuan Qi, Yixin Zhu, Hongjing Lu, Song-Chun Zhu

Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.

Transfer Learning

DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare

no code implementations ICCV 2019 Yuanlu Xu, Song-Chun Zhu, Tony Tung

We present DenseRaC, a novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image.

Ranked #31 on 3D Human Pose Estimation on Human3.6M (using extra training data)

3D Human Pose Estimation

Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns

no code implementations26 Sep 2019 Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

We show that an energy-based spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns.


no code implementations25 Sep 2019 Feng Shi, Yizhou Zhao, Ziheng Xu, Tianyang Liu, Song-Chun Zhu

Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains.

X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust

no code implementations15 Sep 2019 Arjun R. Akula, Changsong Liu, Sari Saba-Sadiya, Hongjing Lu, Sinisa Todorovic, Joyce Y. Chai, Song-Chun Zhu

We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations.

Action Recognition Image Classification +1

Inducing Hierarchical Compositional Model by Sparsifying Generator Network

no code implementations CVPR 2020 Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu

To realize this AND-OR hierarchy in image synthesis, we learn a generator network that consists of the following two components: (i) Each layer of the hierarchy is represented by an over-complete set of convolutional basis functions.

Image Generation Image Reconstruction

Understanding Human Gaze Communication by Spatio-Temporal Graph Reasoning

1 code implementation ICCV 2019 Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu

This paper addresses a new problem of understanding human gaze communication in social videos from both atomic-level and event-level, which is significant for studying human social interactions.

Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense

no code implementations ICCV 2019 Yixin Chen, Siyuan Huang, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu

We propose a new 3D holistic++ scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction---3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation.

3D Human Pose Estimation Human-Object Interaction Detection +1

HUGE2: a Highly Untangled Generative-model Engine for Edge-computing

no code implementations25 Jul 2019 Feng Shi, Ziheng Xu, Tao Yuan, Song-Chun Zhu

In this work, we propose a Highly Untangled Generative-model Engine for Edge-computing or HUGE2 for accelerating these two special convolutions on the edge-computing platform by decomposing the kernels and untangling these smaller convolutions by performing basic matrix multiplications.

Edge-computing Semantic Segmentation

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

2 code implementations29 Mar 2019 Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu

On the other hand, ConvNet potentials learned with non-convergent MCMC do not have a valid steady-state and cannot be considered approximate unnormalized densities of the training data because long-run MCMC samples differ greatly from observed images.

VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning

1 code implementation13 Mar 2019 Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, Song-Chun Zhu

One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.

Natural Language Interaction with Explainable AI Models

no code implementations13 Mar 2019 Arjun R. Akula, Sinisa Todorovic, Joyce Y. Chai, Song-Chun Zhu

This paper presents an explainable AI (XAI) system that provides explanations for its predictions.

RAVEN: A Dataset for Relational and Analogical Visual rEasoNing

no code implementations CVPR 2019 Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, Song-Chun Zhu

In this work, we propose a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation.

Object Recognition Question Answering +2

Visual Discourse Parsing

1 code implementation6 Mar 2019 Arjun R. Akula, Song-Chun Zhu

We propose the task of Visual Discourse Parsing, which requires understanding discourse relations among scenes in a video.

Discourse Parsing Visual Dialog +1

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning

no code implementations7 Feb 2019 Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu

This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e. g., the output is a photo image and the input is a sketch image.

Image-to-Image Translation

Learning vector representation of local content and matrix representation of local motion, with implications for V1

no code implementations24 Jan 2019 Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu

This paper proposes a representational model for image pair such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1).

Inducing Sparse Coding and And-Or Grammar from Generator Network

no code implementations20 Jan 2019 Xianglei Xing, Song-Chun Zhu, Ying Nian Wu

We introduce an explainable generative model by applying sparse operation on the feature maps of the generator network.

Interpretable CNNs for Object Classification

no code implementations8 Jan 2019 Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu

This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part.

General Classification

Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks

no code implementations8 Jan 2019 Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang

In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network.

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model

1 code implementation28 Dec 2018 Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model.

Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

no code implementations27 Dec 2018 Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

The non-linear transformation of this transition model can be parametrized by a feedforward neural network.

Explanatory Graphs for CNNs

no code implementations18 Dec 2018 Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu

This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.

MetaStyle: Three-Way Trade-Off Among Speed, Flexibility, and Quality in Neural Style Transfer

no code implementations13 Dec 2018 Chi Zhang, Yixin Zhu, Song-Chun Zhu

An unprecedented booming has been witnessed in the research area of artistic style transfer ever since Gatys et al. introduced the neural method.

bilevel optimization Style Transfer

Deeper Interpretability of Deep Networks

no code implementations19 Nov 2018 Tian Xu, Jiayu Zhan, Oliver G. B. Garrod, Philip H. S. Torr, Song-Chun Zhu, Robin A. A. Ince, Philippe G. Schyns

However, understanding the information represented and processed in CNNs remains in most cases challenging.

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion

no code implementations ICLR 2019 Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector.

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models

no code implementations9 Oct 2018 Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu

In this paper, we review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models.

Sparse Winograd Convolutional neural networks on small-scale systolic arrays

no code implementations3 Oct 2018 Feng Shi, Haochen Li, Yuhe Gao, Benjamin Kuschner, Song-Chun Zhu

The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators.

Interactive Agent Modeling by Learning to Probe

no code implementations1 Oct 2018 Tianmin Shu, Caiming Xiong, Ying Nian Wu, Song-Chun Zhu

In particular, the probing agent (i. e. a learner) learns to interact with the environment and with a target agent (i. e., a demonstrator) to maximize the change in the observed behaviors of that agent.

Imitation Learning

Human-centric Indoor Scene Synthesis Using Stochastic Grammar

1 code implementation CVPR 2018 Siyuan Qi, Yixin Zhu, Siyuan Huang, Chenfanfu Jiang, Song-Chun Zhu

We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth.

Indoor Scene Synthesis

Learning Human-Object Interactions by Graph Parsing Neural Networks

1 code implementation ECCV 2018 Siyuan Qi, Wenguan Wang, Baoxiong Jia, Jianbing Shen, Song-Chun Zhu

For a given scene, GPNN infers a parse graph that includes i) the HOI graph structure represented by an adjacency matrix, and ii) the node labels.

Human-Object Interaction Detection

Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

1 code implementation ECCV 2018 Siyuan Huang, Siyuan Qi, Yixin Zhu, Yinxue Xiao, Yuanlu Xu, Song-Chun Zhu

We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.

Monocular 3D Object Detection Object Localization +3

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry

2 code implementations16 Jun 2018 Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu

We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner.

Transfer Learning

Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction

no code implementations ICML 2018 Siyuan Qi, Baoxiong Jia, Song-Chun Zhu

Future predictions on sequence data (e. g., videos or audios) require the algorithms to capture non-Markovian and compositional properties of high-level semantics.

Activity Prediction Future prediction

Inferring Shared Attention in Social Scene Videos

no code implementations CVPR 2018 Lifeng Fan, Yixin Chen, Ping Wei, Wenguan Wang, Song-Chun Zhu

We collect a new dataset VideoCoAtt from public TV show videos, containing 380 complex video sequences with more than 492, 000 frames that include diverse social scenes for shared attention study.

Scene Understanding

Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification

1 code implementation CVPR 2018 Wenguan Wang, Yuanlu Xu, Jianbing Shen, Song-Chun Zhu

This paper proposes a knowledge-guided fashion network to solve the problem of visual fashion analysis, e. g., fashion landmark localization and clothing category classification.

General Classification

Unsupervised Learning of Neural Networks to Explain Neural Networks

no code implementations18 May 2018 Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, Song-Chun Zhu

Given feature maps of a certain conv-layer of the CNN, the explainer performs like an auto-encoder, which first disentangles the feature maps into object-part features and then inverts object-part features back to features of higher conv-layers of the CNN.

Learning Descriptor Networks for 3D Shape Synthesis and Analysis

1 code implementation CVPR 2018 Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu

This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns.

3D Object Super-Resolution

Intent-aware Multi-agent Reinforcement Learning

no code implementations6 Mar 2018 Siyuan Qi, Song-Chun Zhu

We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time.

Multi-agent Reinforcement Learning

Building a Telescope to Look Into High-Dimensional Image Spaces

no code implementations2 Mar 2018 Mitch Hill, Erik Nijkamp, Song-Chun Zhu

However, characterizing a learned probability density to uncover the Hopfield memories of the model, encoded by the structure of the local modes, remains an open challenge.

Visual Interpretability for Deep Learning: a Survey

1 code implementation2 Feb 2018 Quanshi Zhang, Song-Chun Zhu

This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.

Explainable artificial intelligence

Examining CNN Representations with respect to Dataset Bias

no code implementations29 Oct 2017 Quanshi Zhang, Wenguan Wang, Song-Chun Zhu

We aim to discover representation flaws caused by potential dataset bias.

Interpretable Convolutional Neural Networks

2 code implementations CVPR 2018 Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu

Instead, the interpretable CNN automatically assigns each filter in a high conv-layer with an object part during the learning process.

single catogory classification

Monocular 3D Human Pose Estimation by Predicting Depth on Joints

no code implementations ICCV 2017 Bruce Xiaohan Nie, Ping Wei, Song-Chun Zhu

This paper aims at estimating full-body 3D human poses from monocular images of which the biggest challenge is the inherent ambiguity introduced by lifting the 2D pose into 3D space.

Depth Estimation Monocular 3D Human Pose Estimation

Jointly Recognizing Object Fluents and Tasks in Egocentric Videos

no code implementations ICCV 2017 Yang Liu, Ping Wei, Song-Chun Zhu

Given an egocentric video, a beam search algorithm is applied to jointly recognizing the object fluents in each frame, and the task of the entire video.

Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling

no code implementations CVPR 2018 Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu

Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image.

A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

no code implementations CVPR 2018 Yuanlu Xu, Lei Qin, Xiaobai Liu, Jianwen Xie, Song-Chun Zhu

We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e. g., from visible to invisible) and track humans in videos.

Visual Tracking

Scene-centric Joint Parsing of Cross-view Videos

no code implementations16 Sep 2017 Hang Qi, Yuanlu Xu, Tao Yuan, Tianfu Wu, Song-Chun Zhu

The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs.

Video Understanding

Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations

no code implementations13 Aug 2017 Quanshi Zhang, Ying Nian Wu, Hao Zhang, Song-Chun Zhu

The loss is defined for nodes in all layers of the AOG, including the generative loss (measuring the likelihood of the images) and the discriminative loss (measuring the fitness to human answers).

Question Answering

Interpreting CNN Knowledge via an Explanatory Graph

no code implementations5 Aug 2017 Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu

Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.

Interactively Transferring CNN Patterns for Part Localization

no code implementations5 Aug 2017 Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Redmonds, Ying Nian Wu, Song-Chun Zhu

In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals.

Predicting Human Activities Using Stochastic Grammar

no code implementations ICCV 2017 Siyuan Qi, Siyuan Huang, Ping Wei, Song-Chun Zhu

This paper presents a novel method to predict future human activities from partially observed RGB-D videos.

Activity Prediction

CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

no code implementations CVPR 2017 Tianmin Shu, Sinisa Todorovic, Song-Chun Zhu

This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities.

Group Activity Recognition

Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars

no code implementations1 Apr 2017 Chenfanfu Jiang, Siyuan Qi, Yixin Zhu, Siyuan Huang, Jenny Lin, Lap-Fai Yu, Demetri Terzopoulos, Song-Chun Zhu

We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms.

Scene Understanding Semantic Segmentation

Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions

no code implementations1 Mar 2017 Tianmin Shu, Xiaofeng Gao, Michael S. Ryoo, Song-Chun Zhu

In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI).

Human robot interaction

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

no code implementations14 Nov 2016 Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu

This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding.

Cooperative Training of Descriptor and Generator Networks

no code implementations29 Sep 2016 Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu

Specifically, within each iteration of the cooperative learning algorithm, the generator model generates initial synthesized examples to initialize a finite-step MCMC that samples and trains the energy-based descriptor model.

Alternating Back-Propagation for Generator Network

no code implementations28 Jun 2016 Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu

This paper proposes an alternating back-propagation algorithm for learning the generator network model.

Modeling and Inferring Human Intents and Latent Functional Objects for Trajectory Prediction

no code implementations24 Jun 2016 Dan Xie, Tianmin Shu, Sinisa Todorovic, Song-Chun Zhu

This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces.

Trajectory Prediction

Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

no code implementations CVPR 2017 Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns.

Inferring Forces and Learning Human Utilities From Videos

no code implementations CVPR 2016 Yixin Zhu, Chenfanfu Jiang, Yibiao Zhao, Demetri Terzopoulos, Song-Chun Zhu

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects.

Motion Planning Robot Task Planning

Attribute And-Or Grammar for Joint Parsing of Human Attributes, Part and Pose

no code implementations6 May 2016 Se-Young Park, Bruce Xiaohan Nie, Song-Chun Zhu

The A-AOG model is an amalgamation of three traditional grammar formulations: (i) Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii) Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii) Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style.

Human Detection Pose Estimation

Learning Social Affordance for Human-Robot Interaction

no code implementations13 Apr 2016 Tianmin Shu, M. S. Ryoo, Song-Chun Zhu

In this paper, we present an approach for robot learning of social affordance from human activity videos.

Human robot interaction

Recognizing Car Fluents from Video

no code implementations CVPR 2016 Bo Li, Tianfu Wu, Caiming Xiong, Song-Chun Zhu

Since there are no publicly related dataset, we collect and annotate a car fluent dataset consisting of car videos with diverse fluents.

A Theory of Generative ConvNet

no code implementations10 Feb 2016 Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu

If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then we obtain a generative ConvNet model that is unique among energy-based models: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process.

Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation

no code implementations15 Dec 2015 Weixin Li, Jungseock Joo, Hang Qi, Song-Chun Zhu

The AOG embeds a context sensitive grammar that can describe the hierarchical composition of news topics by semantic elements about people involved, related places and what happened, and model contextual relationships between elements in the hierarchy.

A Restricted Visual Turing Test for Deep Scene and Event Understanding

no code implementations6 Dec 2015 Hang Qi, Tianfu Wu, Mun-Wai Lee, Song-Chun Zhu

and a sequence of story-line based queries, the task is to provide answers either simply in binary form "true/false" (to a polar query) or in an accurate natural language description (to a non-polar query).

Question Answering Video Captioning +1

Mining And-Or Graphs for Graph Matching and Object Discovery

no code implementations ICCV 2015 Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu

This paper reformulates the theory of graph mining on the technical basis of graph matching, and extends its scope of applications to computer vision.

Graph Matching Graph Mining +1

Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

no code implementations ICCV 2015 Jungseock Joo, Francis F. Steen, Song-Chun Zhu

Secondly, our model can categorize the political party affiliations of politicians, i. e., Democrats vs. Republicans, with the accuracy of 62. 6% (male) and 60. 1% (female).

Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose

no code implementations ICCV 2015 Se-Young Park, Song-Chun Zhu

In this paper, we are interested in developing compositional models to explicit representing pose, parts and attributes and tackling the tasks of attribute recognition, pose estimation and part localization jointly.

Human Parsing Pose Estimation

Learning FRAME Models Using CNN Filters

no code implementations28 Sep 2015 Yang Lu, Song-Chun Zhu, Ying Nian Wu

We explain that each learned model corresponds to a new CNN unit at a layer above the layer of filters employed by the model.

Online Object Tracking, Learning and Parsing with And-Or Graphs

1 code implementation CVPR 2014 Tianfu Wu, Yang Lu, Song-Chun Zhu

In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network.

Object Tracking

Joint Action Recognition and Pose Estimation From Video

no code implementations CVPR 2015 Bruce Xiaohan Nie, Caiming Xiong, Song-Chun Zhu

Action recognition and pose estimation from video are closely related tasks for understanding human motion, most methods, however, learn separate models and combine them sequentially.

Action Recognition Pose Estimation +1

Joint Inference of Groups, Events and Human Roles in Aerial Videos

no code implementations CVPR 2015 Tianmin Shu, Dan Xie, Brandon Rothrock, Sinisa Todorovic, Song-Chun Zhu

This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events.

Video Primal Sketch: A Unified Middle-Level Representation for Video

no code implementations10 Feb 2015 Zhi Han, Zongben Xu, Song-Chun Zhu

This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving primitives to explicitly represent moving corners, lines, feature points, etc., ii) FRAME /MRF model reproducing feature statistics extracted from input video to implicitly represent textured motion, such as water and fire.

Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation

no code implementations29 Jan 2015 Tianfu Wu, Bo Li, Song-Chun Zhu

Firstly, the structure of the And-Or model is learned with three components: (a) mining multi-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining occlusion configurations between single cars, and (c) learning different combinations of part visibility based on car 3D CAD simulation.

Viewpoint Estimation

Mapping Energy Landscapes of Non-Convex Learning Problems

no code implementations2 Oct 2014 Maria Pavlovskaia, Kewei Tu, Song-Chun Zhu

In many statistical learning problems, the target functions to be optimized are highly non-convex in various model spaces and thus are difficult to analyze.

Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

no code implementations CVPR 2014 Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

Given a set of unannotated training images, a dictionary of such hierarchical templates are learned so that each training image can be represented by a small number of templates that are spatially translated, rotated and scaled versions of the templates in the learned dictionary.

Domain Adaptation Template Matching

Single-View 3D Scene Parsing by Attributed Grammar

no code implementations CVPR 2014 Xiaobai Liu, Yibiao Zhao, Song-Chun Zhu

The grammar takes superpixels as its terminal nodes and use five production rules to generate the scene into a hierarchical parse graph.

3D Reconstruction Scene Parsing +1

Learning Inhomogeneous FRAME Models for Object Patterns

no code implementations CVPR 2014 Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

We investigate an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns.

Cross-view Action Modeling, Learning and Recognition

no code implementations CVPR 2014 Jiang wang, Xiaohan Nie, Yin Xia, Ying Wu, Song-Chun Zhu

We present a novel multiview spatio-temporal AND-OR graph (MST-AOG) representation for cross-view action recognition, i. e., the recognition is performed on the video from an unknown and unseen view.

Action Recognition

Unsupervised Structure Learning of Stochastic And-Or Grammars

no code implementations NeurIPS 2013 Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu

Stochastic And-Or grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events.

Joint Video and Text Parsing for Understanding Events and Answering Queries

no code implementations29 Aug 2013 Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe, Song-Chun Zhu

We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph.

Semantic Parsing

Scene Parsing by Integrating Function, Geometry and Appearance Models

no code implementations CVPR 2013 Yibiao Zhao, Song-Chun Zhu

Indoor functional objects exhibit large view and appearance variations, thus are difficult to be recognized by the traditional appearance-based classification paradigm.

Object Recognition Scene Parsing

Discriminatively Trained And-Or Tree Models for Object Detection

no code implementations CVPR 2013 Xi Song, Tianfu Wu, Yunde Jia, Song-Chun Zhu

This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection.

Object Detection

Weakly Supervised Learning for Attribute Localization in Outdoor Scenes

no code implementations CVPR 2013 Shuo Wang, Jungseock Joo, Yizhou Wang, Song-Chun Zhu

We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.

Image Parsing with Stochastic Scene Grammar

no code implementations NeurIPS 2011 Yibiao Zhao, Song-Chun Zhu

The algorithm has two stages: (i) Clustering: It forms all possible higher-level structures (clusters) from lower-level entities by production rules and contextual relations.

Scene Labeling Scene Understanding

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