Xiyang Liu

Long Tian, Yufei Li, Yuyang Dai et al.

Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approaches predominantly focus on deriving prototypes from limited normal samples, they typically neglect to systematically incorporate query image statistics to enhance prototype representativeness. To address this issue, we propose FastRef, a novel and efficient prototype refinement framework for FS-IAD. Our method operates through an iterative two-stage process: (1) characteristic transfer from query features to prototypes via an optimizable transformation matrix, and (2) anomaly suppression through prototype alignment. The characteristic transfer is achieved through linear reconstruction of query features from prototypes, while the anomaly suppression addresses a key observation in FS-IAD that unlike conventional IAD with abundant normal prototypes, the limited-sample setting makes anomaly reconstruction more probable. Therefore, we employ optimal transport (OT) for non-Gaussian sampled features to measure and minimize the gap between prototypes and their refined counterparts for anomaly suppression. For comprehensive evaluation, we integrate FastRef with three competitive prototype-based FS-IAD methods: PatchCore, FastRecon, WinCLIP, and AnomalyDINO. Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and RealIAD demonstrate both the effectiveness and computational efficiency of our approach under 1/2/4-shots.

Haonan Shi, Long Tian, Jie Tao et al.

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.

Ma Zhuoqi, Zhang Yixuan, You Zejun et al.

Artistic image stylization aims to render the content provided by text or image with the target style, where content and style decoupling is the key to achieve satisfactory results. However, current methods for content and style disentanglement primarily rely on image supervision, which leads to two problems: 1) models can only support one modality for style or content input;2) incomplete disentanglement resulting in content leakage from the reference image. To address the above issues, this paper proposes a multimodal approach to content-style disentanglement for artistic image stylization. We construct a \textit{WikiStyle+} dataset consists of artworks with corresponding textual descriptions for style and content. Based on the multimodal dataset, we propose a disentangled representations-guided diffusion model. The disentangled representations are first learned by Q-Formers and then injected into a pre-trained diffusion model using learnable multi-step cross-attention layers. Experimental results show that our method achieves a thorough disentanglement of content and style in reference images under multimodal supervision, thereby enabling more refined stylization that aligns with the artistic characteristics of the reference style. The code of our method will be available upon acceptance.

Yufei Li, Jirui Wu, Long Tian et al.

Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life and health. Despite deep models have made significant advances in capturing the discriminative long-term dependency of surgical videos to achieve improved recognition, they rarely account for exploring and modeling the uncertainty in surgical videos, which should be crucial for reliable online surgical phase recognition. We categorize the sources of uncertainty into two types, frame ambiguity in videos and unbalanced distribution among surgical phases, which are inevitable in surgical videos. To address this pivot issue, we introduce a meta-learning-optimized classification diffusion model (Meta-SurDiff), to take full advantage of the deep generative model and meta-learning in achieving precise frame-level distribution estimation for reliable online surgical phase recognition. For coarse recognition caused by ambiguous video frames, we employ a classification diffusion model to assess the confidence of recognition results at a finer-grained frame-level instance. For coarse recognition caused by unbalanced phase distribution, we use a meta-learning based objective to learn the diffusion model, thus enhancing the robustness of classification boundaries for different surgical phases.We establish effectiveness of Meta-SurDiff in online surgical phase recognition through extensive experiments on five widely used datasets using more than four practical metrics. The datasets include Cholec80, AutoLaparo, M2Cai16, OphNet, and NurViD, where OphNet comes from ophthalmic surgeries, NurViD is the daily care dataset, while the others come from laparoscopic surgeries. We will release the code upon acceptance.

Boan Zhang, Hang Dong, Jiongge Zhang et al.

Traditional range-instantaneous Doppler (RID) methods for rigid-body target imaging often suffer from low resolution due to the limitations of time-frequency analysis (TFA). To address this challenge, our primary focus is on obtaining high resolution time-frequency representations (TFRs) from their low resolution counterparts. Recognizing that the curve features of TFRs are a specific type of texture feature, we argue that pre trained generative models such as Stable Diffusion (SD) are well suited for enhancing TFRs, thanks to their powerful capability in capturing texture representations. Building on this insight, we propose a novel inverse synthetic aperture radar (ISAR) imaging method for rigid-body targets, leveraging the low-rank adaptation (LoRA) of a pre-trained SD model. Our approach adopts the basic structure and pre-trained parameters of SD Turbo while incorporating additional linear operations for LoRA and adversarial training to achieve super-resolution and noise suppression. Then we integrate LoRA-SD into the RID-based ISAR imaging, enabling sharply focused and denoised imaging with super-resolution capabilities. We evaluate our method using both simulated and real radar data. The experimental results demonstrate the superiority of our approach in frequency es timation and ISAR imaging compared to traditional methods. Notably, the generalization capability is verified by training on simulated radar data and testing on measured radar data.

Haichang Gao, Zhongjie Ren, Xiuling Chang et al.

Graphical passwords have been demonstrated to be the possible alternatives to traditional alphanumeric passwords. However, they still tend to follow predictable patterns that are easier to attack. The crux of the problem is users' memory limitations. Users are the weakest link in password authentication mechanism. It shows that baroque music has positive effects on human memorizing and learning. We introduce baroque music to the PassPoints graphical password scheme and conduct a laboratory study in this paper. Results shown that there is no statistic difference between the music group and the control group without music in short-term recall experiments, both had high recall success rates. But in long-term recall, the music group performed significantly better. We also found that the music group tended to set significantly more complicated passwords, which are usually more resistant to dictionary and other guess attacks. But compared with the control group, the music group took more time to log in both in short-term and long-term tests. Besides, it appears that background music does not work in terms of hotspots.

Haichang Gao, Honggang Liu, Dan Yao et al.

CAPTCHAs are employed as a security measure to differentiate human users from bots. A new sound-based CAPTCHA is proposed in this paper, which exploits the gaps between human voice and synthetic voice rather than relays on the auditory perception of human. The user is required to read out a given sentence, which is selected randomly from a specified book. The generated audio file will be analyzed automatically to judge whether the user is a human or not. In this paper, the design of the new CAPTCHA, the analysis of the audio files, and the choice of the audio frame window function are described in detail. And also, some experiments are conducted to fix the critical threshold and the coefficients of three indicators to ensure the security. The proposed audio CAPTCHA is proved accessible to users. The user study has shown that the human success rate reaches approximately 97% and the pass rate of attack software using Microsoft SDK 5.1 is only 4%. The experiments also indicated that it could be solved by most human users in less than 14 seconds and the average time is only 7.8 seconds.

Haichang Gao, Zhongjie Ren, Xiuling Chang et al.

Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.

Xiyang Liu, Zhongjie Ren, Xiuling Chang et al.

The trend toward a highly mobile workforce and the ubiquity of graphical interfaces (such as the stylus and touch-screen) has enabled the emergence of graphical authentications in Personal Digital Assistants (PDAs) [1]. However, most of the current graphical password schemes are vulnerable to shoulder-surfing [2,3], a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Several approaches have been developed to deal with this problem, but they have significant usability drawbacks, usually in the time and effort to log in, making them less suitable for authentication [4, 8]. For example, it is time-consuming for users to log in CHC [4] and there are complex text memory requirements in scheme proposed by Hong [5]. With respect to the scheme proposed by Weinshall [6], not only is it intricate to log in, but also the main claim of resisting shoulder-surfing is proven false [7]. In this paper, we introduce a new graphical password scheme which provides a good resistance to shouldersurfing and preserves a desirable usability.