Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Junxi Chen, Liang Li, Li Su, Zhengjun Zha, Qin Huang
2/9/2026

No confident code implementation found for this paper. 4 possible matches can be shown below.

DOISemantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

100% match2026

Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting

100% match2026

Multi-Factor Adaptive Vision Selection for Egocentric Video Question Answering

100% match2026

Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression

100% match2026

Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction

100% match2026

Text-Conditional Attribute Alignment Across Latent Spaces for 3D Controllable Face Image Synthesis

100% match2026

Cite this paper

@article{chen2026promptenhanced,
  title  = {Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection},
  author = {Junxi Chen and Liang Li and Li Su and Zhengjun Zha and Qin Huang},
  year   = {2026},
  doi    = {10.1109/CVPR52733.2024.01734},
  url    = {https://doi.org/10.1109/CVPR52733.2024.01734},
  journal = {CVPR 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/149de7bed37d01e8e6d154f6d96639dd88987434.svg)](https://codeofpaper.com/paper/149de7bed37d01e8e6d154f6d96639dd88987434)

Discussion