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Zhihao Gu

8 accepted papers

2024

Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection

CVPR 2024poster

Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand most of the state-of-the-art methods have achieved saturation (over…

Cited by 49SourcePDFScholar
2024

Rethinking Reverse Distillation for Multi-Modal Anomaly Detection

AAAI 2024technical

In recent years, there has been significant progress in employing color images for anomaly detection in industrial scenarios, but it is insufficient for identifying anomalies that are invisible in RGB images alone. As a supplement, introducing extra modalities such as depth and surface normal maps c…

Cited by 16SourcePDFScholar
2023

Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection

ICCV 2023poster

Knowledge distillation (KD) has been widely explored in unsupervised anomaly detection (AD). The student is assumed to constantly produce representations of typical patterns within trained data, named "normality", and the representation discrepancy between the teacher and student model is identified…

Cited by 48PDFScholar
2023

Towards Artistic Image Aesthetics Assessment: A Large-Scale Dataset and a New Method

CVPR 2023poster

Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic qual…

2022

Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection

AAAI 2022technical

The rapid development of facial manipulation techniques has aroused public concerns in recent years. Existing deepfake video detection approaches attempt to capture the discrim- inative features between real and fake faces based on tem- poral modelling. However, these works impose supervisions on sp…

Cited by 98SourcePDFScholar
2022

Hierarchical Contrastive Inconsistency Learning for Deepfake Video Detection

ECCV 2022poster

"With the rapid development of Deepfake techniques, the capacity of generating hyper-realistic faces has aroused public concerns in recent years. The temporal inconsistency which derives from the contrast of facial movements between pristine and forged videos can serve as an efficient cue in identif…

Cited by 51SourcePDFScholar
2022

Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection

IJCAI 2022poster

The rapid development of face forgery techniques has drawn growing attention due to security concerns. Existing deepfake video detection methods always attempt to capture the discriminative features by directly exploiting static temporal convolution to mine temporal inconsistency, without explicit…

Cited by 24SourcePDFScholar