🔬 Code and Datasets

🎯 Project Overview

This repository contains the code and resources for our paper KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing (AAAI25).

💻 System Information

• Debian 12
• Python 3.10
• PyTorch 2.3.1
• CUDA 12.5
• Intel(R) Core(TM) i7-9700F CPU @ 3.00GHz
• NVIDIA GeForce RTX 2070 super

🛠 Installation

conda create -n kalahash python=3.10
conda activate kalahash
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt
pip install git+https://github.com/openai/CLIP.git

📊 Dataset

We use the following folder structure.

data
├── cifar-10
│   ├── images
│   ├── train.txt
│   ├── query.txt
│   ├── gallery.txt
│   └── classnames.txt
├── nus-wide
│   ├── images
│   ├── train.txt
│   ├── query.txt
│   ├── gallery.txt
│   └── classnames.txt
└── mscoco
    ├── images
    ├── train.txt
    ├── query.txt
    ├── gallery.txt
    └── classnames.txt

For CIFAR-10, we download the dataset and convert it to RGB images. You can download it from Google Drive.

For NUS-WIDE, we obtain it from DeepHash-pytorch .

For MSCOCO, we use the MS-COCO2014.

To ensure reproducibility, we provide low-resource splits with 1shot_split_cache.pkl, 2shot_split_cache.pkl, 4shot_split_cache.pkl, and 8shot_split_cache.pkl in each dataset folder.

🚀 Usage

To train the model:

python src/main.py experiment=kalahash_cifar10_1s_16b

Experiment configurations can be found in configs/experiment. Results will be saved in outputs/<DATE>.

To evaluate the model:

python src/main.py experiment=kalahash_cifar10_1s_16b test.EVAL_ONLY=True test.CHECKPOINT_PATH=<CHECKPOINT_PATH>

🤖 Model

To ensure reproducibility, we provide model checkpoints.

📄 Citation

@inproceedings{zhao2025kalahash,
  title={KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing},
  author={Zhao, Shu and Yu, Tan and Hao, Xiaoshuai and Ma, Wenchao and Narayanan, Vijaykrishnan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={10},
  pages={10465--10473},
  year={2025}
}

Acknowledgement

CLIP-LoRA

DeepHash-pytorch

ConceptHash

OrthoHash

One Loss For Quantization