This repo is the official implementation of CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation
- We present a simple encoder-decoder to adapt CLIP for open-vocabulary video instance segmentation, called CLIP-VIS. Based on frozen CLIP, our CLIP-VIS retains strong the zero-shot classification ability to various instance categories.
- We design a temporal topK-enhanced matching strategy, which adaptively selects K mostly matching frames to perform query matching.
- We further introduce a weighted open-vocabulary classification module, which refines mask classification by correlating mask prediction and classification.
- Our CLIP-VIS achieves superior performance on multiple datasets. On the LV-VIS validation set, CLIP-VIS outperforms OV2Seg by 5.3% and 11.1% AP using the ResNet50 and ConNext-B backbones, respectively. Furthermore, when evaluating on novel categories, CLIP-VIS achieves a ignificant improvement, outperforming OV2Seg by 12.3% and 23.9% AP, which demonstrates the robust zero-shot classification ability for unseen categories. Additionally, with ResNet50 as the backbone, CLIP-VIS outperforms OpenVIS by 1.6% AP and 1.4% APn on the BURST dataset validation set, further demonstrating its effectiveness.
For further details, please check out our paper.
Please follow installation.
Please follow dataset preperation.
We provide shell scripts for training on image datasets and video datasets. scripts/train.sh trains the model on LVIS or COCO dataset. scripts/train_video.sh fine-tune the model on YTVIS2019 dataset.
To train or evaluate the model in different environments, modify the given shell script and config files accordingly.
sh scripts/train.sh [CONFIG] [NUM_GPUS] [BATCH_SIZE] [OUTPUT_DIR] [OPTS]
sh scripts/train_video.sh [CONFIG] [NUM_GPUS] [BATCH_SIZE] [OUTPUT_DIR] [OPTS]
# Training on LVIS dataset with ResNet50 backbone
sh scripts/train.sh configs/clipvis_R50.yaml 4 8 output/lvis MODEL.MASK_FORMER.DEC_LAYERS 7
#Training on COCO dataset with ResNet50 backbone
sh scripts/train.sh configs/clipvis_R50.yaml 4 8 output/coco MODEL.MASK_FORMER.DEC_LAYERS 10 DATASETS.TRAIN '("coco_2017_train",)' DATASETS.TEST '("coco_2017_val",)'
#Fine-tune on YTVIS2019 dataset with ResNet50 backbone
sh scripts/train_video.sh configs/clipvis_video_R50.yaml 4 8 output/ytvis MODEL.MASK_FORMER.DEC_LAYERS 10 MODEL.WEIGHTS models/coco/model_final.pthWe provide shell scripts scripts/eval_video.sh for Evaluation on various video datasets.
sh scripts/eval_video.sh [CONFIG] [NUM_GPUS] [VAL_DATA] [TEST_NUM_CLASS] [OUTPUT_DIR] [WEIGHTS] [OPTS]
#Evaluation on validation set of LV-VIS datset
sh scripts/eval_video.sh configs/clipvis_video_R50.yaml 4 '("lvvis_val",)' 1196 output/lvvis models/clipvis_lvis_r50_7.pth MODEL.MASK_FORMER.DEC_LAYERS 7We train our network on training set of LVIS dataset and evaluate our network on multiple video datasets. We provide pretrained weights for our models reported in the paper. All of the models were evaluated with 4 NVIDIA 3090 GPUs.
| Training Data | Backbone | LV-VIS val | LV-VIS test | OVIS | YTVIS19 | YTVIS21 | BURST | Download |
|---|---|---|---|---|---|---|---|---|
| LVIS | ResNet-50 | 19.5 | 14.6 | 14.1 | 32.2 | 30.1 | 5.2 | ckpt |
| LVIS | ConvNeXt-B | 32.2 | 25.3 | 18.5 | 42.1 | 37.9 | 8.3 | ckpt |
| COCO,YTVIS19 | ResNet-50 | 9.4 | 6.7 | 15.8 | 39.7 | 35.7 | 4.2 | ckpt |
| COCO,YTVIS19 | ConvNeXt-B | 15.9 | 12.0 | 18.6 | 50.0 | 44.2 | 5.0 | ckpt |
@article{zhu2024clip,
title={CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation},
author={Zhu, Wenqi and Cao, Jiale and Xie, Jin and Yang, Shuangming and Pang, Yanwei},
journal={arXiv preprint arXiv:2403.12455},
year={2024}
}We would like to acknowledge the contributions of public projects, such as Mask2Former, LVVIS and fc-clip whose code has been utilized in this repository.