Open-Vocabulary Panoptic Segmentation with MaskCLIP

By Zheng Ding, Zhuowen Tu. 2022

97 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Zhe Gan
3. Jianwei Yang
4. Chunyuan Li
5. Lijuan Wang
6. Linjie Li
7. Lu Yuan
8. Jianfeng Wang

In Computer Vision and Pattern Recognition

1. Jianfeng Gao
2. Luc Van Gool
3. Dacheng Tao
4. Jianwei Yang
5. Lei Zhang
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Zhe Gan

In Multimodal Learning

1. Zhe Gan
2. Jianfeng Gao
3. Lijuan Wang
4. Chunyuan Li
5. Jianfeng Wang
6. Linjie Li
7. Jianwei Yang
8. Yong Jae Lee

Citations in the corpus

• A Simple Framework for Open-Vocabulary Segmentation and Detection (2023, background): “Many open-vocabulary segmentation models [7, 11, 17, 26] leverages large pretrained vision-language models (e.”
• Generalized Decoding for Pixel, Image, and Language (2022, methodology): “To support open vocabulary recognition, a number of works study how to transfer or distill rich semantic knowledge from imagelevel vision-language foundation models such as CLIP [64] and ALIGN [34] to specialist models [17,25,65].”
• Side Adapter Network for Open-Vocabulary Semantic Segmentation (2023, methodology): “MaskCLIP [10] improves the two-stage framework by progressively refining the predicted masks by the CLIP encoder, and applying masks in attention layers to avoid forwarding multiple times, which was first introduced by [7].”
• Segment Everything Everywhere All at Once (2023, background): “Many recent works proposed to either leverage contrastive learning methods or pretrained multi-modal foundation models ( e.g. , CLIP [8]) to make the segmentation models more transferable to unseen concepts [9, 10, 11, 12]; 2) From generic to referring segmentation.”
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP (2022, unknown): “Pre-trained CLIP has em-powered many computer vision tasks with the language ability, such as image manipulation [34], image generation [10], object detection [17, 42] and image segmentation [11, 12, 16, 21, 23, 31, 39, 40].”