Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning
Authors: Wenxi Lv, Qinliang Su, Wenchao Xu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on MVTec and Vis A demonstrate the superiority of our method in few-shot anomaly detection task under the one-for-all paradigm. Our code is available in https://github.com/Vanssssry/One-For-All-Few-Shot-Anomaly-Detection. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. 2 Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China. 3 Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR |
| Pseudocode | No | The paper describes the methodology using textual explanations and a framework diagram (Figure 1), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available in https://github.com/Vanssssry/One-For-All-Few-Shot-Anomaly-Detection. |
| Open Datasets | Yes | In this paper, we conduct extensive experiments on benchmarks MVTec (Bergmann et al., 2019) and Vis A (Zou et al., 2022). |
| Dataset Splits | Yes | We conduct the comparison experiments between our method and the latest methods under the setting that the training dataset contains all categories of the benchmark and only K-shot normal image for each class. Each class contains at most K examples, where K typically ranges from 1 to 4. |
| Hardware Specification | Yes | All experiments are conducted with a single NVIDIA RTX 3090 24GB GPU. |
| Software Dependencies | No | The paper mentions using 'CLIP (VIT-L-14)', 'BLIP-Diffusion' for Q-Former, and 'Py Torch' for implementation, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The length of learnable prompt T is set as 24. The length of compressed category tokens m is 4. The projection networks used to incorporate visual information are two-layer neural network. The weight factors α, β that balance loss are both fixed as 8. ... We use the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 0.001 to update model parameters. The epoch is 20 for all experiments. |