Awesome

LMPT

🚀Updates

• [Jun 19, 2024] LMPT was accepted by ACL 2024 Workshop on Advances in Language and Vision Research (ALVR).
• [Sep 4, 2023] Added the code for generating captions of images by a pre-trained image-captioning model.
• [May 16, 2023] Uploaded the label-annotation files of the two datasets.
• [May 8, 2023] We released our codes and datasets, including the generated image-caption files.

👀Introduction

This repository contains the code for our paper LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition.[arXiv] [paper]

LMPT explores the feasibility of prompting with text data for long-tailed multi-label visual recognition. We propose a unified framework for LTML, namely prompt tuning with class-specific embedding loss (LMPT), capturing the semantic feature interactions between categories by combining text and image modality data and improving the performance synchronously on both head and tail classes. Specifically, LMPT introduces the embedding loss function with class-aware soft margin and re-weighting to learn class-specific contexts with the benefit of textual descriptions (captions), which could help establish semantic relation ships between classes, especially between the head and tail classes. Notable improvements are observed compared to several visual, zero-shot and prompt tuning methods on two long-tailed multi-label benchmarks. For more details please see the paper.

Created by Peng Xia, Di Xu, ‪Ming Hu‬‬, Lie Ju, and Zongyuan Ge.

💡Requirements

Environment

1. Python 3.8.*
2. CUDA 11.6
3. PyTorch
4. TorchVision

Install

Create a virtual environment and activate it.

conda create -n lmpt python=3.8
conda activate lmpt

The code has been tested with PyTorch 1.13 and CUDA 11.6.

pip install -r requirements.txt

⏳Dataset

To evaluate/train our LMPT network, you will need to download the required datasets. Image paths, labels and captions of each dataset can be found here.

• COCO-LT

• VOC-LT

├── data
    ├── coco
        ├── train2017
            ├── 0000001.jpg
            ...
        ├── val2017
            ├── 0000002.jpg
            ...
        ├── coco_lt_train.txt
        ├── coco_lt_val.txt
        ├── coco_lt_test.txt
        ├── coco_lt_captions.txt
        ├── class_freq.pkl
    ├── voc
        ├── VOCdevkit
            ├── VOC2007
                ├── Annotations
                ├── ImageSets
                ├── JPEGImages
                    ├── 0000001.jpg
                    ...
                ├── SegementationClass
                ├── SegementationObject
            ├── VOC2012
                ├── Annotations
                ├── ImageSets
                ├── JPEGImages
                    ├── 0000002.jpg
                    ...
                ├── SegementationClass
                ├── SegementationObject
        ├── voc_lt_train.txt
        ├── voc_lt_val.txt
        ├── voc_lt_test.txt
        ├── voc_lt_captions.txt
        ├── class_freq.pkl

📦Usage

Train

CUDA_VISIBLE_DEVICES=0 python lmpt/train.py \
--dataset 'voc-lt' \
--seed '0' \
--pretrain_clip 'ViT16' \
--batch_size 64 \
--epochs 50 \
--class_token_position 'end' \
--ctx_init '' \
--n_ctx 16 \
--m_ctx 2 \
--training_method 'lmpt' \
--lr 5e-4 \
--loss_function dbl \
--cseloss softmargin \
--optimizer sgd \
--neg_scale 2.0 \
--gamma 0.2 \
--lam 0.5

Test

CUDA_VISIBLE_DEVICES=0 python lmpt/test.py \
--dataset 'voc-lt' \
--seed '0' \
--pretrain_clip 'ViT16' \
--batch_size 64 \
--class_token_position 'end' \
--ctx_init 'a photo of a' \
--training_method 'lmpt' \
--thre 0.3

Zero-Shot CLIP

CUDA_VISIBLE_DEVICES=0 python zero_shot_clip/test.py \
--dataset 'VOC' \
--nb_classes 20 \
--seed '0' \
--pretrain_clip_path '../pretrained/RN50.pt' \
--dataset 'COCO'
--batch_size 64 \

Fine-Tuning CLIP

CUDA_VISIBLE_DEVICES=0 python finetune_clip/fc.py \
--dataset 'voc-lt' \
--nb_classes 20 \
--seed '0' \
--batch_size 4 \
--pretrain_clip_path '../pretrained/ViT-B-16.pt' \
--dataset 'voc-lt'
--batch_size 100 \
#--from scratch

🙏Acknowledgements

We use code from CoOp and CLIP. We thank the authors for releasing their code.

📧Contact

If you have any questions, please create an issue on this repository or contact at richard.peng.xia@gmail.com or julie334600@gmail.com.

📝Citing

If you find this code useful, please consider to cite our work.

@inproceedings{xia2024lmpt,
  title = {LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-Tailed Multi-Label Visual Recognition},
  author= {Xia, Peng and Xu, Di and Hu, Ming and Ju, Lie and Ge, Zongyuan},
  booktitle={Proceedings of the 3rd Workshop on Advances in Language and Vision Research (ALVR)},
  pages={26--36},
  year={2024}
}