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Pushing the Limit of Post-Training Quantization
Ruihao Gong;Xianglong Liu;Yuhang Li;Yunqiang Fan;Xiuying Wei;Jinyang Guo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Recently, post-training quantization (PTQ) has become the de facto way to produce efficient low-precision neural networks without long-time retraining. Despite its low cost, current PTQ works fail to succeed under the extremely low-bit setting. In this work, we delve into extremely low-bit quantization and construct a unified theoretical analysis, which provides an in-depth understanding of the re...Show More
Video Motion Blur Attack via Grad-Weighted and Discrete-Fusion based Perturbation Generation
IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
Recent research has shown that deep learning networks are vulnerable to adversarial samples. Although there has been great progress in the study of adversarial attacks on images, there is relatively little research on adversarial attacks in the video domain, especially on intrinsic factors of videos, such as motion blur. In this paper, we devise a novel Grad-Weighted based One-step Motion Blur Att...Show More
Hard-Sample Style Guided Patch Attack With RL-Enhanced Motion Pattern for Video Recognition
Jian Yang;Jun Li;Yunong Cai;Guoming Wu;Zhiping Shi;Chaodong Tan;Xianglong Liu
Adversarial attacks have been extensively studied in the image field. In recent years, research has shown that video recognition models are also vulnerable to adversarial examples. However, most studies about adversarial attacks for video models have focused on perturbation-based methods, while patch-based black-box attacks have received less attention. Despite the excellent performance of perturb...Show More
Bullet-Screen-Emoji Attack With Temporal Difference Noise for Video Action Recognition
Yongkang Zhang;Han Zhang;Jun Li;Zhiping Shi;Jian Yang;Kaixin Yang;Shuo Yin;Qiuyan Liang;Xianglong Liu
Recent studies have shown that video action recognition models are also vulnerable to fooling by adversarial samples. However, currently existing video attack methods usually require high computational overhead (e.g., they generate adversarial perturbations for all frames by default), and most of them are difficult to implement printable attacks in the physical world. To address the above issues, ...Show More
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Although deep learning based object detection is of great significance for various applications, it faces challenges when deployed on edge devices due to the computation and energy limitations. Post-training quantization (PTQ) can improve inference efficiency through integer computing. However, they suffer from severe performance degra-dation when performing full quantization due to overlooking th...Show More
PTQ4SAM: Post-Training Quantization for Segment Anything
Chengtao Lv;Hong Chen;Jinyang Guo;Jinyang Guo;Jinyang Guo;Yifu Ding;Xianglong Liu;Xianglong Liu;Xianglong Liu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However, as a large-scale model, the immense memory and computation costs hinder its practical deployment. In this paper, we pro-pose a post-training quantization (PTQ)frameworkfor Segment Anything Model, namely PTQ4SAM. First, we investigate the inherent bottleneck of SAM quantization attributed to the...Show More
NAPGuard: Towards Detecting Naturalistic Adversarial Patches
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Recently, the emergence of naturalistic adversarial patch (NAP), which possesses a deceptive appearance and various representations, underscores the necessity of developing robust detection strategies. However, existing approaches fail to differentiate the deep-seated natures in adversarial patches, i.e., aggressiveness and naturalness, leading to unsatisfactory precision and generalization agains...Show More
LTA-PCS: Learnable Task-Agnostic Point Cloud Sampling
Jiaheng Liu;Jianhao Li;Kaisiyuan Wang;Hongcheng Guo;Jian Yang;Junran Peng;Ke Xu;Xianglong Liu;Jinyang Guo
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Recently, many approaches directly operate on point clouds for different tasks. These approaches become more computation and storage demanding when point cloud size is large. To reduce the required computation and storage, one possible solution is to sample the point cloud. In this paper, we propose the first Learnable Task-Agnostic Point Cloud Sampling (LTA-PCS) framework. Existing task-agnostic ...Show More
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
Yushi Huang;Ruihao Gong;Jing Liu;Tianlong Chen;Xianglong Liu
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
The Diffusion model, a prevalent framework for image generation, encounters significant challenges in terms of broad applicability due to its extended inference times and substantial memory requirements. Efficient Post-training Quantization (PTQ) is pivotal for addressing these issues in traditional models. Different from traditional models, diffusion models heavily depend on the time-step t to ac...Show More
Transferable Multimodal Attack on Vision-Language Pre-training Models
Haodi Wang;Kai Dong;Zhilei Zhu;Haotong Qin;Aishan Liu;Xiaolin Fang;Jiakai Wang;Xianglong Liu
2024 IEEE Symposium on Security and Privacy (SP)
Year: 2024 | Conference Paper |
Cited by: Papers (5)
Vision-Language Pre-training (VLP) models have achieved remarkable success in practice, while easily being misled by adversarial attack. Though harmful, adversarial attacks are valuable in revealing the blind-spots of VLP models and promoting their robustness. However, existing adversarial attacking studies pay insufficient attention to the key roles of different modality-correlated features, lead...Show More
Adversarial Examples Against WiFi Fingerprint-Based Localization in the Physical World
Jiakai Wang;Ye Tao;Yichi Zhang;Wanting Liu;Yusheng Kong;Shaolin Tan;Rongen Yan;Xianglong Liu
WiFi Fingerprint-based Localization (WFL) has recently achieved promising results in the bloom of deep learning techniques. Unfortunately, current studies reveal the great risks of deep-learning models when facing adversarial attacks, raising broader concerns about Deep-learning-based WiFi Fingerprint Localization Models (DFLMs). However, real-world adversarial attacks targeting DFLMs are not full...Show More
Diffusion Patch Attack With Spatial–Temporal Cross-Evolution for Video Recognition
Deep neural networks (DNNs) have demonstrated excellent performance across various domains. However, recent studies have shown that deep neural networks are vulnerable to adversarial examples, including DNN-based video action recognition models. While much of the existing research on adversarial attacks against video models focuses on perturbation-based attacks, there is limited research on patch-...Show More
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection
Simin Li;Huangxinxin Xu;Jiakai Wang;Ruixiao Xu;Aishan Liu;Fazhi He;Xianglong Liu;Dacheng Tao
Billions of people share images from their daily lives on social media every day. However, their biometric information (e.g., fingerprints) could be easily stolen from these images. The threat of fingerprint leakage from social media has created a strong desire to anonymize shared images while maintaining image quality, since fingerprints act as a lifelong individual biometric password. To guard t...Show More
Improving Deepfake Detection Generalization by Invariant Risk Minimization
Zixin Yin;Jiakai Wang;Yisong Xiao;Hanqing Zhao;Tianlin Li;Wenbo Zhou;Aishan Liu;Xianglong Liu
The abuse of deepfake techniques has raised serious concerns about social security and ethical problems, which motivates the development of deepfake detection. However, without fully addressing the domain gap issue, existing deepfake detection methods still show weak generalization ability among datasets belonging to different domains with domain-specific characteristics like identities and genera...Show More
Revisiting Open World Object Detection
Xiaowei Zhao;Yuqing Ma;Duorui Wang;Yifan Shen;Yixuan Qiao;Xianglong Liu
Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. Recently a few studies have introduced and explored the OWOD problem, however, the main challenges in the OWOD task that distinguishing unknown classes from the background (Unknown Objectness...Show More
BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance
Haotong Qin;Xudong Ma;Yifu Ding;Xiaoyang Li;Yang Zhang;Zejun Ma;Jiakai Wang;Jie Luo;Xianglong Liu
Deep neural networks, such as the deep-FSMN, have been widely studied for keyword spotting (KWS) applications while suffering expensive computation and storage. Therefore, network compression technologies such as binarization are studied to deploy KWS models on edge. In this article, we present a strong yet efficient binary neural network for KWS, namely, BiFSMNv2, pushing it to the real-network a...Show More
An innovative experimental teaching method of hardware-software co-design-Taking a hardware accelerator of neural network using FPGA
Ying Li;Jingzhuo Liang;Gui Shi Jie;Yangdong Liu;Wei Huang;Yue Yan;Xianglong Liu
2023 IEEE Frontiers in Education Conference (FIE)
Year: 2023 | Conference Paper |
In order to cultivate students' software and hardware collaborative design thinking and system construction ability, this paper proposes an innovative experimental teaching method of software and hardware collaborative design to better develop students'sys-tem view. We create a student-centered learning environment and adopt a mixed teaching method whereby experiment courses are conducted in a “tr...Show More
Annealing-based Label-Transfer Learning for Open World Object Detection
Yuqing Ma;Hainan Li;Zhange Zhang;Jinyang Guo;Shanghang Zhang;Ruihao Gong;Xianglong Liu
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (16)
Open world object detection (OWOD) has attracted extensive attention due to its practicability in the real world. Previous OWOD works manually designed unknown-discover strategies to select unknown proposals from the background, suffering from uncertainties without appropriate priors. In this paper, we claim the learning of object detection could be seen as an object-level feature-entanglement pro...Show More
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Simin Li;Shuning Zhang;Gujun Chen;Dong Wang;Pu Feng;Jiakai Wang;Aishan Liu;Xin Yi;Xianglong Liu
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect and remove unnatural attacks. However, current studies evaluate naturalness in a case-by-case fashion,...Show More
Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization
Aishan Liu;Shiyu Tang;Siyuan Liang;Ruihao Gong;Boxi Wu;Xianglong Liu;Dacheng Tao
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (6)
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem. Despite the preliminary understandings devoted to adversarially robust generalization, little is known from the architectural...Show More
Imperceptible Adversarial Attack With Multigranular Spatiotemporal Attention for Video Action Recognition
IEEE Internet of Things Journal
Cited by: Papers (4)
In recent years, the application of video Internet of Things (IoT) in various cities and public places has brought unprecedented opportunities to the security field and achieved great success. However, the latest research shows that video recognition models are also vulnerable to adversarial examples, but adversarial examples based on physical attacks are easily detected by humans, making it diffi...Show More
Diverse Sample Generation: Pushing the Limit of Generative Data-Free Quantization
Haotong Qin;Yifu Ding;Xiangguo Zhang;Jiakai Wang;Xianglong Liu;Jiwen Lu
Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalization (BN) statistics of the full-precision networks to quantize the networks. However, it always faces the serious challenges of accuracy degradation in practice. We first give a the...Show More
Temporal Transformer Networks With Self-Supervision for Action Recognition
Yongkang Zhang;Jun Li;Na Jiang;Guoming Wu;Han Zhang;Zhiping Shi;Zhaoxun Liu;Zizhang Wu;Xianglong Liu
IEEE Internet of Things Journal
Cited by: Papers (2)
In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are developing toward intelligence. IoT terminal devices represented by surveillance cameras play an irreplaceable role in modern society, most of them are integrated with video action recognition and other intelligent functions. However, their performance is somewhat affected by the limitation of computing resou...Show More
Spatio-Temporal Adaptive Network With Bidirectional Temporal Difference for Action Recognition
Zhilei Li;Jun Li;Yuqing Ma;Rui Wang;Zhiping Shi;Yifu Ding;Xianglong Liu
Action Recognition is a fundamental task in computer vision field, with a wide range of applications in autonomous driving, security monitoring, etc. However, previous action recognition approaches usually suffer from the inappropriate spatio-temporal modeling or high computational consumption (e.g., 3D CNN). In this paper, we propose a novel Spatio-Temporal Adaptive Network (STANet) with bidirect...Show More
Temporal Speciation Network for Few-Shot Object Detection
Xiaowei Zhao;Xianglong Liu;Yuqing Ma;Shihao Bai;Yifan Shen;Zeyu Hao;Aishan Liu
Recently, few-shot object detection (FSOD) has become an increasing research focus, which can largely alleviate the heavy dependency on expensive annotations in the traditional object detection task. However, existing FSOD approaches fail to generate sufficient high-quality positive region proposals which are the key to detection performance, due to the lack of informative knowledge from base clas...Show More
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