Welcome to the world of visual grounding! In this article, we’ll walk you through using the official PyTorch implementation of the Pseudo-Q framework, as introduced in the CVPR2022 paper **Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding**.

Introduction

The Pseudo-Q method offers a fascinating approach to automatically generate pseudo language queries, aiding in supervised training. This method utilizes an object detector to identify visual objects from unlabeled images and creates language queries for these objects in an unsupervised manner. Essentially, it’s like having an assistant who understands your images without needing to label them beforehand.

Usage

Dependencies

• Python 3.9.10
• PyTorch 1.9.0 + cu111 + cp39
• Pytorch-Bert 0.6.2
• Check requirements.txt for other dependencies.

Data Preparation

Setting up your data is crucial. Here’s how to proceed:

1. Download the images from the original sources such as RefCOCO, ReferItGame, and Flickr30K Entities. Ensure your data folder structure inside .data/image_data looks like this:
• image_data
  • data
    • flickr
    • referit
    • and others…
2. Download and organize your dataset annotations in .data/image_data/dataxxx.
3. Follow the instructions in the README to handle pseudo-samples effectively.

Pretrained Checkpoints

To leverage Pseudo-Q efficiently, download the relevant pretrained checkpoints:

1. DET checkpoints from Tsinghua Cloud.
2. Checkpoints trained on pseudo-samples are also accessible from Tsinghua Cloud.

Training and Evaluation

Training is where the magic happens. You’ll need to execute specific commands for both training and evaluation:

Think of your model like a plant. It needs the right conditions—water (data), sunlight (parameters), and nutrients (hyperparameters)—to grow effectively. The training process involves setting up these conditions properly with:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port 28888 --use_env train.py ...

Refer to train.sh for more comprehensive commands. Similarly, evaluations can be run with:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --master_port 28888 --use_env eval.py ...

Results

Upon successful training, your outputs should showcase various visualizations and experiments demonstrating the efficacy of Pseudo-Q. These results are like the fruits of your labor, proving that the right nurturing can yield splendid outcomes.

Troubleshooting

If you encounter issues during the setup or execution, consider these common troubleshooting tips:

• Ensure all dependencies are correctly installed and the versions match.
• Double-check the directory structure of your data.
• Examine your resource allocation; for example, sufficient GPU availability is crucial.
• If there are persistent issues with data gathers or annotations, feel free to reach out via Issues#2.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.