PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click

By Henghui Ding, Scott D. Cohen, Brian L. Price, Xudong Jiang. 2020

64 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Xiaogang Wang
5. Trevor Darrell
6. Chen Change Loy
7. Alan L. Yuille
8. Jitendra Malik

In Multimodal Learning

1. Henghui Ding
2. Kate Saenko
3. Xudong Jiang
4. Zhe L. Lin
5. Stan Sclaroff
6. Ziwei Liu
7. Jianming Zhang
8. Kai Chen

Citations in the corpus

• Segment Anything (2023, background): “When SAM fails to pick the right object from a text prompt only, an additional point often fixes the prediction, similar to [31].”
• Reviving Iterative Training with Mask Guidance for Interactive Segmentation (2021, methodology): “PhraseClick [27] combines clicks with text input to better infer the attributes of the target object, thus requiring fewer clicks.”
• PseudoClick: Interactive Image Segmentation with Click Imitation (2022, background): “Although clicks are the main form of input in interactive segmentation, many works have explored other types of user interaction, including bounding boxes [13], scribbles [23], and interactions from multiple modalities [24].”
• Vision-Language Transformer and Query Generation for Referring Segmentation (2021, background): “Referring segmentation targets to generate segmentation mask for the target object referred by a given query expression in natural language [10, 16, 15, 3].”
• Interactive Object Segmentation With Inside-Outside Guidance (2022, background): “[81], [82] use natural language as feedback for interactive segmentation correction.”
• Transformer-Based Visual Segmentation: A Survey (2023, background): “• Results on Video Panoptic Segmentation: In Table XIII, for VPS, SLOT-VPS [360] achieves the best results on Cityscapes-VPS.”
• Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation (2020, background): “As a fundamental problem in computer vision, this task has attracted a lot of attention from the research community and achieved great success along with the development of deep learning in recent years [7, 8, 14, 15, 19, 24, 38, 45, 50, 51, 57, 59, 60, 63, 66].”
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling (2020, background): “Deep learning has achieved great success on various dense prediction problems [14, 15, 13, 12, 42, 43, 45, 44, 36].”