[CVPR2024] Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation(CLIP-RC).

This is the PyTorch implementation of the paper : Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation.

[Paper #1] [BibTeX]

Abstract

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However, it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper, to solve the mentioned challenge, we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations, we propose a novel method for zero-shot semantic segmentation, dubbed CLIP-RC (CLIP with Regional Clues), bringing two main insights. On the one hand, a region-level bridge is necessary to provide fine-grained semantics. On the other hand, overfitting should be mitigated during the training stage. Benefiting from the above discoveries, CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks, including PASCAL VOC, PASCAL Context, and COCO-Stuff 164K.

Table of contents

Installation

1. Clone the repository

   git clone https://github.com/uyzhang/CLIP-RC

2. Install the dependencies

   pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html
   pip install -r requirements.txt
   mim install mmcv-full==1.4.4
   pip install mmsegmentation==0.24.0

3. Download the ViT-B-16 of the CLIP pre-training model from OpenAI

Data Preparation

Please refer to mmsegmentation to prepare data

Pretrained models

Dataset	Setting	pAcc	mIoU(S)	mIoU(U)	hIoU	Model Zoo
PASCAL VOC 2012	Inductive	95.8	92.8	84.4	88.4	[Google Drive]
PASCAL VOC 2012	Transductive	97.0	93.9	92.2	93.0	[Google Drive]
PASCAL VOC 2012	Fully	97.1	94.1	93.4	93.7	[Google Drive]
COCO Stuff 164K	Inductive	63.1	40.9	41.6	41.2	[Google Drive]
COCO Stuff 164K	Transductive	69.9	42.0	60.8	49.7	[Google Drive]
COCO Stuff 164K	Fully	70.8	42.9	64.1	51.4	[Google Drive]

Usage

Inductive Training

# for example
bash dist_train.sh configs/voc12/clip_rc_zero_vit-b_512x512_40k_voc_10_16.py work_dirs/clip_rc_zero_vit-b_512x512_40k_voc_10_16 2

Transductive Training

# for example
bash dist_train.sh configs/voc12/clip_rc_zero_vit-b_512x512_20k_voc_10_16_st.py work_dirs/clip_rc_zero_vit-b_512x512_20k_voc_10_16_st 2 --load-from work_dirs/clip_rc_zero_vit-b_512x512_40k_voc_10_16/iter_20000.pth

Fully supervised Training

# for example
bash dist_train.sh configs/voc12/clip_rc_fully_vit-b_512x512_40k_voc_10_16.py work_dirs/clip_rc_fully_vit-b_512x512_40k_voc_10_16 2

Test

# for example
python test.py configs/voc12/clip_rc_zero_vit-b_512x512_40k_voc_10_16.py work_dirs/voc_zero.pth --eval mIoU

Parameters and FLOPs

python get_flops.py configs/voc12/clip_rc_zero_vit-b_512x512_40k_voc_10_16.py --shape 512 512 --fvcore

Acknowledgement

Our work is closely related to the following assets that inspire our implementation. We gratefully thank the authors.

• CLIP
• ZegCLIP
• Visual Prompt Tuning

Citing CLIP-RC

If you find it helpful, you can cite our paper in your work.

@InProceedings{Zhang_2024_CVPR,
    author    = {Zhang, Yi and Guo, Meng-Hao and Wang, Miao and Hu, Shi-Min},
    title     = {Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {3270-3280}
}