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..
Towards Multi-Domain Learning for Generalizable Video Anomaly Detection
Authors: MyeongAh Cho, Taeoh Kim, Minho Shim, Dongyoon Wee, Sangyoun Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results |
| Researcher Affiliation | Collaboration | Myeong Ah Cho Kyung Hee University EMAIL Taeoh Kim NAVER Cloud EMAIL Minho Shim NAVER Cloud EMAIL Dongyoon Wee NAVER Cloud EMAIL Sangyoun Lee Yonsei University EMAIL |
| Pseudocode | No | The paper includes 'Table A4: Illustration of each layer of proposed model' which details network architecture, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | We will release the data and code when the paper is accepted. |
| Open Datasets | Yes | In this paper, we use six representative VAD datasets: UCF-Crimes (UCFC) [43], XD-Violences (XD) [51], Large-scale Anomaly Detection (LAD) [47], UBI-Fights (UBIF) [9], Traffic Anomaly Dataset (TAD) [19], and Shanghai-Tech Campus (ST) [24]. |
| Dataset Splits | Yes | To achieve this, we sampled each dataset to align with the dataset with the smallest volume, ensuring that each abnormal category has a similar proportion and conducted 3-fold experiments. |
| Hardware Specification | Yes | All models and experiments are implemented and evaluated end-to-end using Py Torch [34] with a single NVIDIA V100 GPU. |
| Software Dependencies | No | All models and experiments are implemented and evaluated end-to-end using Py Torch [34] with a single NVIDIA V100 GPU. |
| Experiment Setup | Yes | The hyper-parameters are T = 32, λ = 10, τ = 0.3, and m = 0.3. Since the single-head baseline cannot assign AC labels using Eq. 6, pseudo-labels for the AC classifier are assigned based on the range of predicted abnormal scores. |