AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal Samples

Few-shot Anomaly Detection (FAD) poses significant challenges due to the limited availability of training samples and the frequent absence of abnormal samples. Previous approaches often rely on annotations or true abnormal samples to improve detection, but such textual or visual cues are not always accessible. To address this, we introduce AnoPLe, a multi-modal prompt learning method designed for anomaly detection without prior knowledge of anomalies. AnoPLe simulates anomalies and employs bidirectional coupling of textual and visual prompts to facilitate deep interaction between the two modalities. Additionally, we integrate a lightweight decoder with a learnable multi-view signal, trained on multi-scale images to enhance local semantic comprehension. To further improve performance, we align global and local semantics, enriching the image-level understanding of anomalies. The experimental results demonstrate that AnoPLe achieves strong FAD performance, recording 94.1% and 86.2% Image AUROC on MVTec-AD and VisA respectively, with only around a 1% gap compared to the SoTA, despite not being exposed to true anomalies. Code is available at https://github.com/YoojLee/AnoPLe.

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.

SelFormaly: Towards Task-Agnostic Unified Anomaly Detection

The core idea of visual anomaly detection is to learn the normality from normal images, but previous works have been developed specifically for certain tasks, leading to fragmentation among various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly clustering. This one-task-one-model approach is resource-intensive and incurs high maintenance costs as the number of tasks increases. This paper presents SelFormaly, a universal and powerful anomaly detection framework. We emphasize the necessity of our off-the-shelf approach by pointing out a suboptimal issue with fluctuating performance in previous online encoder-based methods. In addition, we question the effectiveness of using ConvNets as previously employed in the literature and confirm that self-supervised ViTs are suitable for unified anomaly detection. We introduce back-patch masking and discover the new role of top k-ratio feature matching to achieve unified and powerful anomaly detection. Back-patch masking eliminates irrelevant regions that possibly hinder target-centric detection with representations of the scene layout. The top k-ratio feature matching unifies various anomaly levels and tasks. Finally, SelFormaly achieves state-of-the-art results across various datasets for all the aforementioned tasks.