Zilong Zheng's Homepage

2022/01	One paper is accepted in ICLR’22 as an Oral paper!
2021/12	One paper is accepted for Oral presentation in AAAI’22!
2021/05	I am awarded as CVPR Outstanding Reviewer for CVPR’21.
2021/03	Three papers are accepted in CVPR’21, including two Oral papers!

Latest

Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling Oral ICLR'22

Bo Wan, Wenjuan Han, Zilong Zheng, and Tinne Tuytelaars

The Tenth International Conference on Learning Representations (ICLR) , 2022

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We introduce a new task, unsupervised vision-language (VL) grammar induction. Given an image-caption pair, the goal is to extract a shared hierarchical structure for both image and language simultaneously. We argue that such structured output, grounded in both modalities, is a clear step towards the high-level understanding of multimodal information. Besides challenges existing in conventional visually grounded grammar induction tasks, VL grammar induction requires a model to capture contextual semantics and perform a fine-grained alignment. To address these challenges, we propose a novel method, CLIORA, which constructs a shared vision-language constituency tree structure with context-dependent semantics for all constituents in different levels of the tree. It computes a matching score between each constituent and image region, trained via contrastive learning. It integrates two levels of fusion, namely at feature-level and at score-level, so as to allow fine-grained alignment. We introduce a new evaluation metric: Critical Concept Recall Rate (CCRR) to explicitly evaluate VL grammar induction, and show a 2.6% improvement over a strong baseline on Flickr30k Entities. We also evaluate our model via two derived tasks, i.e., language grammar induction and phrase grounding, and improve over the state-of-the-art for both.

@article{wan2022unsupervised, title={Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling}, author={Wan, Bo and Han, Wenjuan and Zheng, Zilong and Tuytelaars, Tinne}, journal={The Tenth International Conference on Learning Representations (ICLR)}, year={2022} }

2022

Energy-Based Generative Cooperative Saliency Prediction Oral AAAI'22

Jing Zhang, Jianwen Xie, Zilong Zheng, and Nick Barnes

The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI) , 2022

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Conventional saliency prediction models typically learn a deterministic mapping from images to the corresponding ground truth saliency maps. In this paper, we study the saliency prediction problem from the perspective of generative models by learning a conditional probability distribution over saliency maps given an image, and treating the prediction as a sampling process. Specifically, we propose a generative cooperative saliency prediction framework based on the generative cooperative networks, where a conditional latent variable model and a conditional energy-based model are jointly trained to predict saliency in a cooperative manner. We call our model the SalCoopNets. The latent variable model serves as a fast but coarse predictor to efficiently produce an initial prediction, which is then refined by the iterative Langevin revision of the energy-based model that serves as a fine predictor. Such a coarse-to-fine cooperative saliency prediction strategy offers the best of both worlds. Moreover, we generalize our framework to the scenario of weakly supervised saliency prediction, where saliency annotation of training images is partially observed, by proposing a cooperative learning while recovering strategy. Lastly, we show that the learned energy function can serve as a refinement module that can refine the results of other pretrained saliency prediction models. Experimental results show that our generative model can achieve state-of-the-art performance.

@article{zhang2022energy, title = {Energy-Based Generative Cooperative Saliency Prediction}, author = {Zhang, Jing and Xie, Jianwen and Zheng, Zilong and Barnes, Nick}, journal={The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)}, year = {2022} }

2021

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Multi-Modal Conditional Learning TPAMI

Jianwen Xie*, Zilong Zheng*, Xiaolin Fang, Song-Chun Zhu, and Ying Nian Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2021

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This paper studies the supervised learning of the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different modalities, e.g., the output is an photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various multi-modal conditional learning tasks, e.g., class-to-image generation, image-to-image translation, and image recovery.

@article{xie2021cooperative, title={Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Multi-Modal Conditional Learning}, author={Xie, Jianwen and Zheng, Zilong and Fang, Xiaolin and Zhu, Song-Chun and Wu, Ying Nian}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, abbr={TPAMI}, year={2021} }
Learning Triadic Belief Dynamics in Nonverbal Communication from Videos Oral CVPR'21

Lifeng Fan, Shuwen Qiu, Zilong Zheng, Tao Gao, Song-Chun Zhu, and Yixin Zhu

In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2021

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Humans possess a unique social cognition capability; nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communication cues (e.g., gaze, human poses, and gestures) to represent, model, learn, and infer agents’ mental states from pure visual inputs. Crucially, such a mental representation takes the agent’s belief into account so that it represents what the true world state is and infers the beliefs in each agent’s mental state, which may differ from the true world states. By aggregating different beliefs and true world states, our model essentially forms “five minds” during the interactions between two agents. This “five minds” model differs from prior works that infer beliefs in an infinite recursion; instead, agents’ beliefs are converged into a “common mind”. Based on this representation, we further devise a hierarchical energybased model that jointly tracks and predicts all five minds. From this new perspective, a social event is interpreted by a series of nonverbal communication and belief dynamics, which transcends the classic keyframe video summary. In the experiments, we demonstrate that using such a social account provides a better video summary on videos with rich social interactions compared with state-of-the-art keyframe video summary methods

@inproceedings{fan2021learning, title = {Learning Tradic Belief Dynamics in Nonverbal Communication from Videos}, author = {Lifeng Fan and Shuwen Qiu and Zilong Zheng and Tao Gao and Song-Chun Zhu and Yixin Zhu}, year = {2021}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)} }
Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning Oral CVPR'21

Zilong Zheng, Jianwen Xie, and Ping Li

In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2021

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Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the internal distribution of patches within the image without relying on external training data. Different from prior works that model such a distribution implicitly with a top-down latent variable model (e.g., generator), this paper proposes to explicitly represent the statistical distribution within a single natural image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Meanwhile, a coarse-to-fine sequential training and sampling strategy is presented to train the model efficiently. Besides learning to generate random samples from white noise, the model can learn in parallel with a self-supervised task (e.g., recover the input image from its corrupted version), which can further improve the descriptive power of the learned model. The proposed model is simple and natural in that it does not require an auxiliary model (e.g., discriminator) to assist the training. Besides, it also unifies internal statistics learning and image generation in a single framework. Experimental results presented on various image generation and manipulation tasks, including super-resolution, image editing, harmonization, style transfer, etc., have demonstrated the effectiveness of our model for internal learning.

@inproceedings{zheng2021patchgencn, title={Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning}, author={Zheng, Zilong and Xie, Jianwen and Li, Ping}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)}, year={2021} }
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification CVPR'21

Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu

In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2021

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We propose a generative model of unordered point sets, such as point clouds, in the forms of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into energy for the whole point cloud. We show that our model can be derived from the discriminative PointNet. The model can be trained by MCMC-based maximum likelihood learning (as well as its variants), without the help of any assisting networks like those in GANs and VAEs. Unlike most point cloud generator that relys on hand-crafting distance metrics, our model does not rely on hand-crafting distance metric for point cloud generation, because it synthesizes point clouds by matching observed examples in terms of statistical property defined by the energy function. Furthermore, we can learn a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpretation. The learned point cloud representation can be also useful for point cloud classification. Experiments demonstrate the advantages of the proposed generative model of point clouds.

@inproceedings{xie2021GPointent, title={Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification}, author={Xie, Jianwen and Xu, Yifei and Zheng, Zilong and Zhu, Song-Chun and Wu, Ying Nian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)}, year={2021} }
GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning ACL'21

Zilong Zheng, Shuwen Qiu, Lifeng Fan, Yixin Zhu, and Song-Chun Zhu

Findings of ACL, ACL-IJCNLP , 2021

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Understanding what we genuinely mean instead of what we literally say in conversations is challenging for both humans and machines; yet, this direction is mostly left untouched in modern open-ended dialogue systems. To fill in this gap, we present a grammar-based dialogue dataset, GRICE, designed to bring implicature into pragmatic reasoning in the context of conversations. Our design of GRICE also incorporates other essential aspects of modern dialogue modeling (e.g., coreference). The entire dataset is systematically generated using a hierarchical grammar model, such that each dialogue context has intricate implicatures and is temporally consistent. We further present two tasks, the implicature recovery task followed by the pragmatic reasoning task in conversation, to evaluate the model's reasoning capability. In experiments, we adopt baseline methods that claimed to have pragmatics reasoning capability; the results show a large performance gap between baseline methods and human performance. After integrating a simple module that explicitly reasons about implicature, the model shows an overall performance boost in conversational reasoning. These observations demonstrate the significance of implicature recovery for open-ended dialogue reasoning and call for future research in conversational implicature and conversational reasoning.

@article{zheng2021implicature, title={GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational Reasoning}, author={Zheng, Zilong and Qiu, Shuwen and Fan, Lifeng and Zhu, Yixin and Zhu, Song-Chun}, journal={Findings of ACL, ACL-IJCNLP}, year={2021} }
Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler AAAI'21

Jianwen Xie, Zilong Zheng, and Ping Li

The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) , 2021

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Due to the intractable partition function, training energybased models (EBMs) by maximum likelihood requires Markov chain Monte Carlo (MCMC) sampling to approximate the gradient of the Kullback–Leibler divergence between data and model distributions. However, it is non-trivial to sample from an EBM because of the difficulty of mixing between modes. In this paper, we propose to learn a variational auto-encoder (VAE) to initialize the finite-step MCMC, such as Langevin dynamics that is derived from the energy function, for efficient amortized sampling of the EBM. With these amortized MCMC samples, the EBM can be trained by maximum likelihood, which follows an “analysis by synthesis” scheme; while the variational auto-encoder learns from these MCMC samples via variational Bayes. We call this joint training algorithm the variational MCMC teaching, in which the VAE chases the EBM toward data distribution. We interpret the learning algorithm as a dynamic alternating projection in the context of information geometry. Our proposed models can generate samples comparable to GANs and EBMs. Additionally, we demonstrate that our models can learn effective probabilistic distribution toward supervised conditional learning experiments.

@article{xie2021vaeebm, title={Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler}, author={Xie, Jianwen and Zheng, Zilong and Li, Ping}, journal={The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI)}, year={2021} }
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation AAAI'21

Jianwen Xie*, Zilong Zheng*, Xiaolin Fang, Song-Chun Zhu, and Ying Nian Wu

The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) , 2021

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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. The use of generative cooperative network enables maximum likelihood learning of the domain model by MCMC teaching, where the energy-based model seeks to fit the data distribution of domain and distills its knowledge to the latent variable model via MCMC. Specifically, in the MCMC teaching process, the latent variable model parameterized by an encoder-decoder maps examples from the source domain to the target domain, while the energy-based model further refines the mapped results by Langevin revision such that the revised results match to the examples in the target domain in terms of the statistical properties, which are defined by the learned energy function. For the purpose of building up a correspondence between two unpaired domains, the proposed framework simultaneously learns a pair of cooperative networks with cycle consistency, accounting for a two-way translation between two domains, by alternating MCMC teaching. Experiments show that the proposed framework is useful for unsupervised image-to-image translation and unpaired image sequence translation.

@article{xie2021cycle, title={Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation}, author={Xie, Jianwen and Zheng, Zilong and Fang, Xiaolin and Zhu, Song-Chun and Wu, Ying Nian}, journal={The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)}, year={2021} }

2020

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis TPAMI

Jianwen Xie*, Zilong Zheng*, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2020

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3D data that contains rich geometry information of objects and scenes is a valuable asset for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a 3D shape descriptor network, which is a deep 3D convolutional energy-based model, for representing volumetric shape patterns. The maximum likelihood training of the model follows an “analysis by synthesis” scheme. The benefits of the proposed model are five-fold: first, unlike GANs and VAEs, the training of the model does not rely on any auxiliary models; second, the model can synthesize realistic 3D shapes by sampling from the probability distribution via MCMC, such as Langevin dynamics; third, the conditional version of the model can be applied to 3D object recovery and super-resolution; fourth, the model can be used to train a 3D generator network via MCMC teaching; fifth, the unsupervisedly trained model provides a powerful feature extractor for 3D data, which can be useful for 3D object classification. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns and can be useful for a wide variety of 3D shape analysis.

@article{xie2020gvoxelnet, title={Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis}, author= {Xie, Jianwen and Zheng, Zilong and Gao, Ruiqi and Wang, Wenguan and Zhu, Song-Chun and Wu, Ying Nian}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, year={2020} }
Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs ICRA'20

Tao Yuan, Hangxin Liu, Lifeng Fan, Zilong Zheng, Tao Gao, Yixin Zhu, and Song-Chun Zhu

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) , 2020

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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. Specifically, a parse graph (PG) is learned from a single-view spatiotemporal parsing by aggregating various object states along the time; such a learned representation is accumulated as the robot’s knowledge. An inference algorithm is derived to fuse individual PG from all robots across multi-views into a joint PG, which affords more effective reasoning and inference capability to overcome the errors originated from a single view. In the experiments, through the joint inference over PGs, the system correctly recognizes human (false-)belief in various settings and achieves better cross-view accuracy on a challenging small object tracking dataset.

@inproceedings{yuan2020joint, title={Joint Inference of States, Robot Knowledge, and Human (False-)Beliefs}, author={Yuan, Tao and Liu, Hangxin and Fan, Lifeng and Zheng, Zilong and Gao, Tao and Zhu, Yixin and Zhu, Song-Chun}, booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)}, year={2020} }
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns Oral AAAI'20

Jianwen Xie*, Ruiqi Gao*, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) , 2020

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Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factorial components. A commonly used model for dynamic patterns is the state space model, where the state evolves over time according to a transition model and the state generates the observed image frames according to an emission model. To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels. Thus in the emission model, we let the hidden state generate the displacement field, which warps the trackable component in the previous image frame to generate the next frame while adding a simultaneously emitted residual image to account for the change that cannot be explained by the deformation. The warping of the previous image is about the trackable part of the change of image frame, while the residual image is about the intrackable part of the image. We use a maximum likelihood algorithm to learn the model parameters that iterates between inferring latent noise vectors that drive the transition model and updating the parameters given the inferred latent vectors. Meanwhile we adopt a regularization term to penalize the norms of the residual images to encourage the model to explain the change of image frames by trackable motion. Unlike existing methods on dynamic patterns, we learn our model in unsupervised setting without ground truth displacement fields or optical flows. In addition, our model defines a notion of intrackability by the separation of warped component and residual component in each image frame. We show that our method can synthesize realistic dynamic pattern, and disentangling appearance, trackable and intrackable motions. The learned models can be useful for motion transfer, and it is natural to adopt it to define and measure intrackability of a dynamic pattern.

@article{xie2020motion, title={Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns}, author={Xie, Jianwen and Gao, Ruiqi and Zheng, Zilong and Zhu, Song-Chun and Wu, Ying Nian}, journal={The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)}, year={2020} }

2019

Reasoning Visual Dialogs with Structural and Partial Observations Oral CVPR'19

Zilong Zheng*, Wenguan Wang*, Siyuan Qi*, and Song-Chun Zhu

In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2019

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We propose a novel model to address the task of Visual Dialog which exhibits complex dialog structures. To obtain a reasonable answer based on the current question and the dialog history, the underlying semantic dependencies between dialog entities are essential. In this paper, we explicitly formalize this task as inference in a graphical model with partially observed nodes and unknown graph structures (relations in dialog). The given dialog entities are viewed as the observed nodes. The answer to a given question is represented by a node with missing value. We first introduce an Expectation Maximization algorithm to infer both the underlying dialog structures and the missing node values (desired answers). Based on this, we proceed to propose a differentiable graph neural network (GNN) solution that approximates this process. Experiment results on the VisDial and VisDial-Q datasets show that our model outperforms comparative methods. It is also observed that our method can infer the underlying dialog structure for better dialog reasoning.

@inproceedings{zheng2019reasoning, title={Reasoning Visual Dialogs with Structural and Partial Observations}, author={Zheng, Zilong and Wang, Wenguan and Qi, Siyuan and Zhu, Song-Chun}, booktitle={Computer Vision and Pattern Recognition (CVPR), 2019 IEEE Conference on}, year={2019} }
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time Spotlight AAAI'19

Jianwen Xie*, Ruiqi Gao*, Zilong Zheng, Song-Chun Zhu, and Ying Nian Wu

The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) , 2019

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This paper studies the dynamic generator model for spatial-temporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transformation is parametrized by a top-down neural network. The sequence of latent state vectors follows a non-linear auto-regressive model, where the state vector of the next frame is a non-linear transformation of the state vector of the current frame as well as an independent noise vector that provides randomness in the transition. The non-linear transformation of this transition model can be parametrized by a feedforward neural network. We show that this model can be learned by an alternating back-propagation through time algorithm that iteratively samples the noise vectors and updates the parameters in the transition model and the generator model. We show that our training method can learn realistic models for dynamic textures and action patterns.

@article{xie2019DG, title = {Learning Dynamic Generator Model by Alternating Back-Propagation Through Time}, author = {Xie, Jianwen and Gao, Ruiqi and Zheng, Zilong and Zhu, Song-Chun and Wu, Ying Nian}, journal={The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI)}, year = {2019} }

2018

Learning Descriptor Networks for 3D Shape Synthesis and Analysis Oral CVPR'18

Jianwen Xie*, Zilong Zheng*, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu

In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , 2018

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This paper proposes a 3D shape descriptor network, which is a deep convolutional energy-based model, for modeling volumetric shape patterns. The maximum likelihood training of the model follows an “analysis by synthesis” scheme and can be interpreted as a mode seeking and mode shifting process. The model can synthesize 3D shape patterns by sampling from the probability distribution via MCMC such as Langevin dynamics. The model can be used to train a 3D generator network via MCMC teaching. The conditional version of the 3D shape descriptor net can be used for 3D object recovery and 3D object super-resolution. Experiments demonstrate that the proposed model can generate realistic 3D shape patterns and can be useful for 3D shape analysis.

@inproceedings{xie2018learning, title={Learning Descriptor Networks for 3D Shape Synthesis and Analysis}, author={Xie, Jianwen and Zheng, Zilong and Gao, Ruiqi and Wang, Wenguan and Zhu, Song-Chun and Wu, Ying Nian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)}, pages={8629--8638}, year={2018} }

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