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Official code for Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions (CVPR 2024)
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Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions

This repository contains the code for the paper Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions (CVPR 2024).

News

Setup

  1. Create a new conda environment:

    conda create --name vg python=3.8
conda activate vg

  2. Install pytorch following the official website. We have successfully tested our code on pytorch 2.2.1 (conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia), but lower version should also be feasible.

  3. Install pip requirements via:

    pip install -r requirements.txt

Data Download

RefCOCO/g/+

We use the same pre-processed data for RefCOCO/g/+ as ReCLIP for a fair comparison. Download pre-processed data files via gsutil cp gs://reclip-sanjays/reclip_data.tar.gz, and extract the data using tar -xvzf reclip_data.tar.gz. This data does not include images, therefore also download the images for RefCOCO/g/+ from http://images.cocodataset.org/zips/train2014.zip.

Caption Triplets

We release the triplets files in Huggingface

LoRA Checkpoints

They are also in Huggingface.

VLA Fine-tuning Data

For CLIP/FLAVA fine-tuning, you need to download the following datasets:

  1. HICO-det
  2. SWiG
  3. VG

Please follow the instructions provided on each website to download the raw data.

After downloading the datasets, modify the configuration file VLA_finetune/training/data_path.ini according to the root directory of your downloaded data.

For the triplets_file required for SWiG, it can be found in Huggingface (SWiG_triplets.json)

Results with CLIP/FLAVA on RefCOCO/g/+

The following format can be used to run experiments:

python eval_refcoco/main.py --input_file [INPUT_FILE] --image_root [IMAGE_ROOT] --method [parse/baseline/random/matching] --clip_model ViT-B/32 --triplets_file [TRIPLETS_FILE] --detector_file [DETECTION_FILE] {--rule_filter} {--enable_lora} {--lora_path [LORA_PATH]}

(/ is used above to denote different options for a given argument. Content enclosed in brackets [] should be replaced with the actual content, while braces {} denote optional arguments.)

--input_file: should be in .jsonl format, e.g., reclip_preprocess/refcocog_val.jsonl (we provide these files for the datasets same as ReCLIP; see the Data Download information above).

--image_root: the top-level directory containing all images in the dataset, e.g., COCO2014/train2014/

--detector_file: if not specified, ground-truth proposals are used. The detection files are in reclip_data.tar.gz and have the format {refcoco/refcocog/refcoco+}_dets_dict.json, e.g., reclip_preprocess/refcocog_dets_dict.json

--triplets_file: caption triplets generated using ChatGPT, e.g., triplets/gpt_refcocog_val.jsonl

--rule_filter: Whether to filter out redundant triplets based on heuristic rules. Should be activated on default.

--enable_lora: Whether to load pre-trained LoRA modules for CLIP and FLAVA.

--lora_path: If enable_lora, you should specify the path to the pre-trained LoRA module, e.g., pre_trained/epoch_latest.pt

Choices for method: "matching" stands for our proposed triplet-to-instance visual grounding pipeline. "parse", "baseline" are two grounding methods used in the baseline paper ReCLIP. "random" selects one of the proposals uniformly at random.

Choices for clip_model: We only use ViT-B/32 for entire experiments.

To see explanations of other arguments see the eval_refcoco/main.py file.

How to Generate Caption Triplets

  1. Install Gentopia by running the following command:

    pip install gentopia

  2. Modify the ROOT_PATH and DATA_BASE_PATH variables in the triplets_chatgpt/scripts/run.sh file. Ensure that the paths point to the correct directories. (For reclip_preprocess, see the Data Download information above).

  3. Make sure to add your OpenAI key in the triplets_chatgpt/.env file for authentication.

  4. After making the necessary modifications, run the bash file run.sh to generate the caption triplets.

How to Fine-tune CLIP/FLAVA

Make sure the data is ready for VLA fine-tuning.

The following format can be used to fine-tune CLIP:

torchrun --nproc_per_node 8 --master_port 23450 -m VLA_finetune.training.main --name CLIP_finetune --lora 4 --pretrained openai --epochs 20 --warmup 150 --workers 48 --lr 0.000005 --save-frequency 5 --batch-size 128 --model ViT-B/32

The following format can be used to fine-tune FLAVA:

torchrun --nproc_per_node 8 --master_port 23452 -m VLA_finetune.training.main --name FLAVA_finetune --lora 16 --epochs 20 --warmup 150 --workers 48 --lr 0.000005 --save-frequency 5 --batch-size 128 --flava

TODO

Acknowledgements

The code in the eval_refcoco directory is adapted from the baseline method ReCLIP to ensure a fair comparison. We have removed code pertaining to other comparison methods originally present in ReCLIP repo to enhance readability. The code in the VLA_finetune directory is adapted from code in TSVLC.

Citation

If you find this repository useful, please cite our paper:

@article{han2023zero,
  title={Zero-shot Referring Expression Comprehension via Structural Similarity Between Images and Captions},
  author={Han, Zeyu and Zhu, Fangrui and Lao, Qianru and Jiang, Huaizu},
  journal={arXiv preprint arXiv:2311.17048},
  year={2023}
}