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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
INFP: INdustrial Video Anomaly Detection via Frequency Prioritization
Authors: Qianzi Yu, Kai Zhu, Yang Cao, Yu Kang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the benchmark IPAD dataset demonstrate the superiority of our proposed method over the state-of-the-art. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, Hefei, China 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the method using mathematical equations and textual descriptions, but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | IPAD[Liu et al., 2024] is the first video anomaly detection dataset that focuses on industrial scenarios, which contains a total of 597,979 frames, with 430,867 frames allocated for training data and 167,112 frames for the test data. |
| Dataset Splits | Yes | IPAD... contains a total of 597,979 frames, with 430,867 frames allocated for training data and 167,112 frames for the test data. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | Each video frame is resized as 224 288, the intensity of which is normalized to the range of [ 1, 1] before being fed into the model. The learning rate is set as 2e-4 initially and decrease to 1e-4 at epoch 120. The Adam optimizer is used to train our network. A sequence of five video frames is randomly selected from the training set, with the first four frames serving as input and the fifth frame serving as the ground truth. |