Zhongfei Zhang

Binghamton University

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i-10 index

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Yingming Li

Zhejiang University

Xi Li

Nanchang Institute of Science & Technology

Zhong Ji

Tianjin University

Yunlong Yu

Zhejiang University

Fei Wu

Zhejiang University

Ruofei Zhang

Henan Academy of Sciences

Yueting Zhuang

Zhejiang University of Science and Technology

Philip S. Yu

University of Illinois Chicago

Weiming Hu

Chinese Academy of Sciences

Siyu Huang

Sun Yat-sen University

Cooperative Institutions

Zhejiang University

Chinese Academy of Sciences

Tianjin University

Zhejiang University of Science and Technology

University of Chinese Academy of Sciences

University of Science and Technology of China

Sun Yat-sen University

Tsinghua University

Nanjing University

University of Electronic Science and Technology of China

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Multitask Non-Autoregressive Model for Human Motion Prediction

IEEE Transactions on Image Processing (2020)

Bin Li Jian Tian Zhongfei Zhang Hailin Feng Xi Li

Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been devoted to exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation problem. In this paper, we argue that such issue is mainly caused by adopting autoregressive manner. Hence, a novel Non-AuToregressive model (NAT) is proposed with a complete non-autoregressive decoding scheme, as well as a context encoder and a positional encoding module. More specifically, the context encoder embeds the given poses from temporal and spatial perspectives. The frame decoder is responsible for predicting each future pose independently. The positional encoding module injects positional signal into the model to indicate the temporal order. Besides, a multitask training paradigm is presented for both low-level human skeleton prediction and high-level human action recognition, resulting in the considerable improvement for the prediction task. Our approach is evaluated on Human3.6M and CMU-Mocap benchmarks and outperforms state-of-the-art autoregressive methods.

ENCODE

Sequence (biology)

10.1109/tip.2020.3038362

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Citations (40)

Deep Structured Energy Based Models for Anomaly Detection

arXiv (Cornell University) (2016)

Shuangfei Zhai Yu Cheng Weining Lu Zhongfei Zhang

In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure. We develop novel model architectures to integrate EBMs with different types of data such as static data, sequential data, and spatial data, and apply appropriate model architectures to adapt to the data structure. Our training algorithm is built upon the recent development of score matching \cite{sm}, which connects an EBM with a regularized autoencoder, eliminating the need for complicated sampling method. Statistically sound decision criterion can be derived for anomaly detection purpose from the perspective of the energy landscape of the data distribution. We investigate two decision criteria for performing anomaly detection: the energy score and the reconstruction error. Extensive empirical studies on benchmark tasks demonstrate that our proposed model consistently matches or outperforms all the competing methods.

Autoencoder

Benchmark (surveying)

Anomaly (physics)

10.48550/arxiv.1605.07717

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Citations (104)

Approximate Image Mappings Between Nearly Boresight Aligned Optical and Range Sensors.

J. Ross Beveridge Zhongfei Zhang M Goss Mark R. Stevens Anna Schwickerath

This technical report summarizes the intrinsic sensor parameters for the range, IR and color sensor used in the Fort Carson data collection and explores sensor-to-sensor image mapping under di erent assumptions regarding relative sensor placement.The default case is to assume perfectly boresight aligned placement, and then the implications of di erent deviations from this perfect placement are considered.Included in this report is a description of the calibration process used to recover the color sensor parameters.A k ey result shown in detail is the relative equivalence of planar sensor translation and small angle pan and tilt for points of known depth.This simplifying approximation has signi cant implications when fusing data from separate range and optical sensors.

10.21236/ada308479

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Citations (5)

Graph-Free Knowledge Distillation for Graph Neural Networks

Xiang Deng Zhongfei Zhang

Knowledge distillation (KD) transfers knowledge from a teacher network to a student by enforcing the student to mimic the outputs of the pretrained teacher on training data. However, data samples are not always accessible in many cases due to large data sizes, privacy, or confidentiality. Many efforts have been made on addressing this problem for convolutional neural networks (CNNs) whose inputs lie in a grid domain within a continuous space such as images and videos, but largely overlook graph neural networks (GNNs) that handle non-grid data with different topology structures within a discrete space. The inherent differences between their inputs make these CNN-based approaches not applicable to GNNs. In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a GNN without graph data. The proposed graph-free KD (GFKD) learns graph topology structures for knowledge transfer by modeling them with multinomial distribution. We then introduce a gradient estimator to optimize this framework. Essentially, the gradients w.r.t. graph structures are obtained by only using GNN forward-propagation without back-propagation, which means that GFKD is compatible with modern GNN libraries such as DGL and Geometric. Moreover, we provide the strategies for handling different types of prior knowledge in the graph data or the GNNs. Extensive experiments demonstrate that GFKD achieves the state-of-the-art performance for distilling knowledge from GNNs without training data.

10.24963/ijcai.2021/320

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Citations (35)

GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning

arXiv (Cornell University) (2018)

Siyu Huang Xi Li Zhi-Qi Cheng Zhongfei Zhang Alexander G. Hauptmann

A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures. Typically, the conventional deep multi-attribute learning approaches follow the pipeline of manually designing the network architectures based on task-specific expertise prior knowledge and careful network tunings, leading to the inflexibility for various complicated scenarios in practice. Motivated by addressing this problem, we propose an efficient greedy neural architecture search approach (GNAS) to automatically discover the optimal tree-like deep architecture for multi-attribute learning. In a greedy manner, GNAS divides the optimization of global architecture into the optimizations of individual connections step by step. By iteratively updating the local architectures, the global tree-like architecture gets converged where the bottom layers are shared across relevant attributes and the branches in top layers more encode attribute-specific features. Experiments on three benchmark multi-attribute datasets show the effectiveness and compactness of neural architectures derived by GNAS, and also demonstrate the efficiency of GNAS in searching neural architectures.

GNAS complex locus

Benchmark (surveying)

ENCODE

10.48550/arxiv.1804.06964

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Citations (16)

AI’s 10 to Watch for 2024

IEEE Intelligent Systems (2025)

Jürgen Dix Zhongfei Zhang

10.1109/mis.2024.3517893

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Citations (0)

A Semantics-Guided Class Imbalance Learning Model for Zero-Shot Classification

arXiv (Cornell University) (2019)

Zhong Ji Xue‐jie Yu Yunlong Yu Yanwei Pang Zhongfei Zhang

Zero-Shot Classification (ZSC) equips the learned model with the ability to recognize the visual instances from the novel classes via constructing the interactions between the visual and the semantic modalities. In contrast to the traditional image classification, ZSC is easily suffered from the class-imbalance issue since it is more concerned with the class-level knowledge transfer capability. In the real world, the class samples follow a long-tailed distribution, and the discriminative information in the sample-scarce seen classes is hard to be transferred to the related unseen classes in the traditional batch-based training manner, which degrades the overall generalization ability a lot. Towards alleviating the class imbalance issue in ZSC, we propose a sample-balanced training process to encourage all training classes to contribute equally to the learned model. Specifically, we randomly select the same number of images from each class across all training classes to form a training batch to ensure that the sample-scarce classes contribute equally as those classes with sufficient samples during each iteration. Considering that the instances from the same class differ in class representativeness, we further develop an efficient semantics-guided feature fusion model to obtain discriminative class visual prototype for the following visual-semantic interaction process via distributing different weights to the selected samples based on their class representativeness. Extensive experiments on three imbalanced ZSC benchmark datasets for both the Traditional ZSC (TZSC) and the Generalized ZSC (GZSC) tasks demonstrate our approach achieves promising results especially for the unseen categories those are closely related to the sample-scarce seen categories.

Discriminative model

Feature (linguistics)

Benchmark (surveying)

Sample (material)

Contextual image classification

10.48550/arxiv.1908.09745

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Citations (2)

Learning with Retrospection

Proceedings of the AAAI Conference on Artificial Intelligence (2021)

Xiang Deng Zhongfei Zhang

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarded information indeed useless? We argue that the discarded information can benefit the subsequent training. In this paper, we propose learning with retrospection (LWR) which makes use of the learned information in the past epochs to guide the subsequent training. LWR is a simple yet effective training framework to improve accuracies, calibration, and robustness of DNNs without introducing any additional network parameters or inference cost, but only with a negligible training overhead. Extensive experiments on several benchmark datasets demonstrate the superiority of LWR for training DNNs.

Deep Neural Networks

Robustness

Benchmark (surveying)

Training set

10.1609/aaai.v35i8.16885

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Citations (11)

Doubly Convolutional Neural Networks

arXiv (Cornell University) (2016)

Shuangfei Zhai Yu Cheng Weining Lu Zhongfei Zhang

Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea. In stead of allocating a set of convolutional filters that are independently learned, a DCNN maintains groups of filters where filters within each group are translated versions of each other. Practically, a DCNN can be easily implemented by a two-step convolution procedure, which is supported by most modern deep learning libraries. We perform extensive experiments on three image classification benchmarks: CIFAR-10, CIFAR-100 and ImageNet, and show that DCNNs consistently outperform other competing architectures. We have also verified that replacing a convolutional layer with a doubly convolutional layer at any depth of a CNN can improve its performance. Moreover, various design choices of DCNNs are demonstrated, which shows that DCNN can serve the dual purpose of building more accurate models and/or reducing the memory footprint without sacrificing the accuracy.

Convolution (computer science)

Memory footprint

Contextual image classification

Footprint

10.48550/arxiv.1610.09716

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Citations (20)

Spectral clustering for multi-type relational data

Bo Long Zhongfei Zhang Wu Xiaoyun Philip S. Yu

Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-commerce and bioinformatics. However, the research on general multi-type relational data clustering is still limited and preliminary. The contribution of the paper is three-fold. First, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Second, under this model, we derive a novel algorithm, the spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed algorithm. Third, we show that the existing spectral clustering algorithms can be considered as the special cases of the proposed model and algorithm. This demonstrates the good theoretic generality of the proposed model and algorithm.

10.1145/1143844.1143918

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Citations (262)