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Zhixiang Wang

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8 papers
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8

NeurIPS Conference 2025 Conference Paper

DOVTrack: Data-Efficient Open-Vocabulary Tracking

  • Zekun Qian
  • Ruize Han
  • Zhixiang Wang
  • Junhui Hou
  • Wei Feng

Open-Vocabulary Multi-Object Tracking (OVMOT) aims to detect and track multi-category objects including both seen and unseen categories during training. Currently, a significant challenge in this domain is the lack of large-scale annotated video data for training. To address this challenge, this work aims to effectively train the OV tracker using only the existing limited and sparsely annotated video data. We propose a comprehensive training sample space expansion strategy that addresses the fundamental limitation of sparse annotations in OVMOT training. Specifically, for the association task, we develop a diffusion-based feature generation framework that synthesizes intermediate object features between sparsely annotated frames, effectively expanding the training sample space by approximately 3× and enabling robust association learning from temporally continuous features. For the detection task, we introduce a dynamic group contrastive learning approach that generates diverse sample groups through affinity, dispersion, and adversarial grouping strategies, tripling the effective training samples for classification while maintaining sample quality. Additionally, we propose an adaptive localization loss that expands positive sample coverage by lowering IoU thresholds while mitigating noise through confidence-based weighting. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the OVMOT benchmark, surpassing existing methods by 3. 8\% in TETA metric, without requiring additional data or annotations. The code will be available at https: //github. com/zekunqian/DOVTrack.

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NeurIPS Conference 2025 Conference Paper

Sekai: A Video Dataset towards World Exploration

  • Zhen Li
  • Chuanhao Li
  • Xiaofeng Mao
  • Shaoheng Lin
  • Ming Li
  • Shitian Zhao
  • Zhaopan Xu
  • Xinyue Li

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5, 000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Comprehensive analyses and experiments demonstrate the dataset’s scale, diversity, annotation quality, and effectiveness for training video generation models. We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

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IROS Conference 2024 Conference Paper

Asynchronous Event-Inertial Odometry using a Unified Gaussian Process Regression Framework

  • Xudong Li
  • Zhixiang Wang
  • Zihao Liu 0004
  • Yizhai Zhang
  • Fan Zhang 0031
  • Xiuming Yao
  • Panfeng Huang

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

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AAAI Conference 2024 Conference Paper

Contributing Dimension Structure of Deep Feature for Coreset Selection

  • Zhijing Wan
  • Zhixiang Wang
  • Yuran Wang
  • Zheng Wang
  • Hongyuan Zhu
  • Shin'ichi Satoh

Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training dataset sizes. Sample selection hinges on two main aspects: a sample's representation in enhancing performance and the role of sample diversity in averting overfitting. Existing methods typically measure both the representation and diversity of data based on similarity metrics, such as L2-norm. They have capably tackled representation via distribution matching guided by the similarities of features, gradients, or other information between data. However, the results of effectively diverse sample selection are mired in sub-optimality. This is because the similarity metrics usually simply aggregate dimension similarities without acknowledging disparities among the dimensions that significantly contribute to the final similarity. As a result, they fall short of adequately capturing diversity. To address this, we propose a feature-based diversity constraint, compelling the chosen subset to exhibit maximum diversity. Our key lies in the introduction of a novel Contributing Dimension Structure (CDS) metric. Different from similarity metrics that measure the overall similarity of high-dimensional features, our CDS metric considers not only the reduction of redundancy in feature dimensions, but also the difference between dimensions that contribute significantly to the final similarity. We reveal that existing methods tend to favor samples with similar CDS, leading to a reduced variety of CDS types within the coreset and subsequently hindering model performance. In response, we enhance the performance of five classical selection methods by integrating the CDS constraint. Our experiments on three datasets demonstrate the general effectiveness of the proposed method in boosting existing methods.

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NeurIPS Conference 2024 Conference Paper

Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection

  • Hui Wei
  • Zhixiang Wang
  • Kewei Zhang
  • Jiaqi Hou
  • Yuanwei Liu
  • Hao Tang
  • Zheng Wang

Physical adversarial attacks can deceive deep neural networks (DNNs), leading to erroneous predictions in real-world scenarios. To uncover potential security risks, attacking the safety-critical task of person detection has garnered significant attention. However, we observe that existing attack methods overlook the pivotal role of the camera, involving capturing real-world scenes and converting them into digital images, in the physical adversarial attack workflow. This oversight leads to instability and challenges in reproducing these attacks. In this work, we revisit patch-based attacks against person detectors and introduce a camera-agnostic physical adversarial attack to mitigate this limitation. Specifically, we construct a differentiable camera Image Signal Processing (ISP) proxy network to compensate for the physical-to-digital transition gap. Furthermore, the camera ISP proxy network serves as a defense module, forming an adversarial optimization framework with the attack module. The attack module optimizes adversarial patches to maximize effectiveness, while the defense module optimizes the conditional parameters of the camera ISP proxy network to minimize attack effectiveness. These modules engage in an adversarial game, enhancing cross-camera stability. Experimental results demonstrate that our proposed Camera-Agnostic Patch (CAP) attack effectively conceals persons from detectors across various imaging hardware, including two distinct cameras and four smartphones.

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IROS Conference 2024 Conference Paper

Self-reconfiguration Strategies for Space-distributed Spacecraft

  • Tianle Liu
  • Zhixiang Wang
  • Yongwei Zhang
  • Ziwei Wang 0001
  • Zihao Liu 0004
  • Yizhai Zhang
  • Panfeng Huang

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-orbit self-reconfiguration algorithms play a crucial role in realizing the benefits of distributed spacecraft. This paper adopts the framework of imitation learning combined with reinforcement learning for strategy learning of module handling order. A robot arm motion algorithm is then designed to execute the handling sequence. We achieve the self-reconfiguration handling task by creating a map on the surface of the module, completing the path point planning of the robotic arm using A*. The joint planning of the robotic arm is then accomplished through forward and reverse kinematics. Finally, the results are presented in Unity3D.

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AAAI Conference 2023 Conference Paper

HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design

  • Hui Wei
  • Zhixiang Wang
  • Xuemei Jia
  • Yinqiang Zheng
  • Hao Tang
  • Shin'ichi Satoh
  • Zheng Wang

Adversarial attacks on thermal infrared imaging expose the risk of related applications. Estimating the security of these systems is essential for safely deploying them in the real world. In many cases, realizing the attacks in the physical space requires elaborate special perturbations. These solutions are often impractical and attention-grabbing. To address the need for a physically practical and stealthy adversarial attack, we introduce HotCold Block, a novel physical attack for infrared detectors that hide persons utilizing the wearable Warming Paste and Cooling Paste. By attaching these readily available temperature-controlled materials to the body, HotCold Block evades human eyes efficiently. Moreover, unlike existing methods that build adversarial patches with complex texture and structure features, HotCold Block utilizes an SSP-oriented adversarial optimization algorithm that enables attacks with pure color blocks and explores the influence of size, shape, and position on attack performance. Extensive experimental results in both digital and physical environments demonstrate the performance of our proposed HotCold Block. Code is available: https://github.com/weihui1308/HOTCOLDBlock.

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IJCAI Conference 2020 Conference Paper

Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

  • Zheng Wang
  • Zhixiang Wang
  • Yinqiang Zheng
  • Yang Wu
  • Wenjun Zeng
  • Shin'ichi Satoh

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i. e. , the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https: //github. com/lightChaserX/Awesome-Hetero-reID

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