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Found 6 papers, 3 papers with code
Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
The minimax excess risk optimization (MERO) problem is a new variation of the traditional distributionally robust optimization (DRO) problem, which achieves uniformly low regret across all test distributions under suitable conditions.
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Finally, we report the results of popular IAD methods on the Real-IAD dataset, providing a highly challenging benchmark to promote the development of the IAD field.
Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAID), which consists of 60, 337 artistic images covering various art forms, with more than 360, 000 votes from online users.
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information.
Ranked #16 on
Anomaly Detection
on MVTec AD
Spatiotemporal Inconsistency Learning for DeepFake Video Detection
To address this issue, we term this task as a Spatial-Temporal Inconsistency Learning (STIL) process and instantiate it into a novel STIL block, which consists of a Spatial Inconsistency Module (SIM), a Temporal Inconsistency Module (TIM), and an Information Supplement Module (ISM).
A Novel Retinex-Based Fractional-Order Variational Model for Image with Severely Low Light
The proposed method is more flexible in controlling the reg- ularization extent than the existing integer-order regularization methods.
