PRVQL: Progressive Knowledge-Guided Refinement for Robust Egocentric Visual Query Localization

🎉 This work has been accepted to ICCV 2025!

📝 Overview

Egocentric Visual Query Localization (EgoVQL) aims to locate a target object in both space and time within first-person videos based on a given visual query. However, existing methods often struggle with significant appearance variations and cluttered backgrounds, leading to reduced localization accuracy.

To overcome these challenges, PRVQL introduces a progressive knowledge-guided refinement approach. By dynamically extracting and refining knowledge from the video itself, PRVQL continuously enhances query and video features across multiple stages, resulting in more accurate localization.

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🔍 Core Idea

PRVQL employs appearance and spatial knowledge extraction modules at each stage to iteratively refine the query and video features. This progressive refinement leads to increasingly accurate localization results.

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🏗️ Model Framework

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⚙️ Environment Setup

Set up your environment with the following commands:

conda create --name prvql python=3.8 -y
conda activate prvql

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge

pip install -r requirements.txt

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📦 Pretrained Weights

Download the pretrained model weights from Google Drive and place them in:

./output/ego4d_vq2d/train/train

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📂 Dataset Preparation

1️⃣ Process the Dataset

Follow the instructions in the VQLoC repository to process the dataset into video clips and images.

2️⃣ Organize the Dataset

Ensure the dataset is structured as follows:

./your/dataset/path/
└── datav2
    ├── clips
    │   ├── 1.mp4
    │   └── ...
    ├── images
    │   ├── 1   
    │   │   ├── 1.mp4
    │   │   └── ...
    │   └── ...        
    ├── train_annot.json
    ├── val_annot.json
    ├── vq_test_unannotated.json
    ├── vq_train.json
    └── vq_val.json

3️⃣ Update Configuration Files

Modify the dataset path in the following configuration files:

• config/eval.yaml
• config/train.yaml
• config/val.yaml

Update the dataset root path:

root: './your/dataset/path/'

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🏋️ Training & Evaluation

We will release the model and code soon.

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📊 Benchmark Results on Ego4D Validation and Test Sets

📈 Validation Set

Method	tAP$_{25}$	stAP$_{25}$	rec%	Succ
STARK (ICCV'21)	0.10	0.04	12.41	18.70
SiamRCNN (CVPR'22)	0.22	0.15	32.92	43.24
NFM (VQ2D'22)	0.26	0.19	37.88	47.90
CocoFormer (CVPR'23)	0.26	0.19	37.67	47.68
VQLoC (NeurIPS'23)	0.31	0.22	47.05	55.89
PRVQL (Ours)	0.35	0.27	47.87	57.93

🏆 Test Set

Method	tAP$_{25}$	stAP$_{25}$	rec%	Succ
STARK (ICCV'21)	-	-	-	-
SiamRCNN (CVPR'22)	0.20	0.13	-	-
NFM (VQ2D'22)	0.24	0.17	-	-
CocoFormer (CVPR'23)	0.25	0.18	-	-
VQLoC (NeurIPS'23)	0.32	0.24	45.11	55.88
PRVQL (Ours)	0.37	0.28	45.70	59.43

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📖 Citation

If you find this repository useful, please consider starring ⭐ it and citing our work:

@article{fan2025prvql,
  title={PRVQL: Progressive Knowledge-Guided Refinement for Robust Egocentric Visual Query Localization},
  author={Fan, Bing and Feng, Yunhe and Tian, Yapeng and Lin, Yuewei and Huang, Yan and Fan, Heng},
  journal={arXiv preprint arXiv:2502.07707},
  year={2025}
}

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