Towards Multi-Domain Learning for Generalizable Video Anomaly Detection
Authors: MyeongAh Cho, Taeoh Kim, Minho Shim, Dongyoon Wee, Sangyoun Lee
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results |
| Researcher Affiliation | Collaboration | Myeong Ah Cho Kyung Hee University maycho@khu.ac.kr Taeoh Kim NAVER Cloud taeoh.kim@navercorp.com Minho Shim NAVER Cloud minho.shim@navercorp.com Dongyoon Wee NAVER Cloud dongyoon.wee@navercorp.com Sangyoun Lee Yonsei University syleee@yonsei.ac.kr |
| 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. |