# High-Quality Pseudo-Label Generation Based on Visual Prompt Assisted Cloud Model Update

arXiv: https://arxiv.org/abs/2504.00526 · HTML: https://mlsanity.com/paper/2504.00526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2211.13218/) (2022)
- [Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation](https://mlsanity.com/paper/2212.04145/) (2022)
- [Unsupervised Domain Adaptation of Object Detectors: A Survey](https://mlsanity.com/paper/2105.13502/) (2021)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers](https://mlsanity.com/paper/2012.10557/) (2020)
- [Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers](https://mlsanity.com/paper/2107.12636/) (2021)
- [Real-Time Video Inference on Edge Devices via Adaptive Model Streaming](https://mlsanity.com/paper/2006.06628/) (2020)
- [AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference](https://mlsanity.com/paper/2105.04104/) (2021)
- [Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world](https://mlsanity.com/paper/2212.00972/) (2022)
- [Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning](https://mlsanity.com/paper/2306.15333/) (2023)
- [Update Compression for Deep Neural Networks on the Edge](https://mlsanity.com/paper/2203.04516/) (2022)
- [A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment](https://mlsanity.com/paper/2403.10299/) (2024)
- [EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices](https://mlsanity.com/paper/2308.08717/) (2023)

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

- [Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay](https://mlsanity.com/paper/2507.00042/) (2025)

## Abstract (arXiv)

Generating high-quality pseudo-labels on the cloud is crucial for cloud-edge object detection, especially in dynamic traffic monitoring where data distributions evolve. Existing methods often assume reliable cloud models, neglecting potential errors or struggling with complex distribution shifts. This paper proposes Cloud-Adaptive High-Quality Pseudo-label generation (CA-HQP), addressing these limitations by incorporating a learnable Visual Prompt Generator (VPG) and dual feature alignment into cloud model updates. The VPG enables parameter-efficient adaptation by injecting visual prompts, enhancing flexibility without extensive fine-tuning. CA-HQP mitigates domain discrepancies via two feature alignment techniques: global Domain Query Feature Alignment (DQFA) capturing scene-level shifts, and fine-grained Temporal Instance-Aware Feature Embedding Alignment (TIAFA) addressing instance variations. Experiments on the Bellevue traffic dataset demonstrate that CA-HQP significantly improves pseudo-label quality compared to existing methods, leading to notable performance gains for the edge model and showcasing CA-HQP's adaptation effectiveness. Ablation studies validate each component (DQFA, TIAFA, VPG) and the synergistic effect of combined alignment strategies, highlighting the importance of adaptive cloud updates and domain adaptation for robust object detection in evolving scenarios. CA-HQP provides a promising solution for enhancing cloud-edge object detection systems in real-world applications.