# Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models

arXiv: https://arxiv.org/abs/2403.15943 · HTML: https://mlsanity.com/paper/2403.15943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Attention-Enhancing Backdoor Attacks Against BERT-based Models](https://mlsanity.com/paper/2310.14480/) (2023)
- [Particle Filter SLAM for Vehicle Localization](https://mlsanity.com/paper/2402.07429/) (2024)
- [Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance](https://mlsanity.com/paper/2310.02107/) (2023)
- [LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection](https://mlsanity.com/paper/2401.16001/) (2024)
- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [Jointly Learning Selection Matrices For Transmitters, Receivers And Fourier Coefficients In Multichannel Imaging](https://mlsanity.com/paper/2402.19023/) (2024)
- [Joint Spatio-Temporal Precoding for Practical Non-Stationary Wireless Channels](https://mlsanity.com/paper/2211.06017/) (2022)

## Built on by (most cited first, 1 of 1)

- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)

## Abstract (arXiv)

Change detection is a fundamental task in computer vision that processes a bi-temporal image pair to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets, and employ an auxiliary network to detect changes. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance.