Human-in-the-loop image segmentation and annotation

Zhang, Xiaoya; Wang, Lianjie; Xie, Jin; Zhu, Pengfei

doi:10.1007/s11432-019-2759-y

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The field of interactive image segmentation has been explored for nearly two decades. Unlike automatic segmentation methods such as semantic segmentation [15], interactive methods take advantage of human participation [16] to segment a specified object. Such methods focus on two issues: interaction mode and back-end algorithm.…”

Section: Image Interactive Segmentationmentioning

confidence: 99%

Sequential interactive image segmentation

et al. 2023

View full text Add to dashboard Cite

Interactive image segmentation (IIS) is an important technique for obtaining pixel-level annotations. In many cases, target objects share similar semantics. However, IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects, previous user interaction, and previous prediction masks, which can all provide suitable priors for the current annotation. In this paper, we formulate a sequential interactive image segmentation (SIIS) task for minimizing user interaction when segmenting sequences of related images, and we provide a practical approach to this task using two pertinent designs. The first is a novel interaction mode. When annotating a new sample, our method can automatically propose an initial click proposal based on previous annotation. This dramatically helps to reduce the interaction burden on the user. The second is an online optimization strategy, with the goal of providing semantic information when annotating specific targets, optimizing the model with dense supervision from previously labeled samples. Experiments demonstrate the effectiveness of regarding SIIS as a particular task, and our methods for addressing it.

show abstract

Section: Image Interactive Segmentationmentioning

confidence: 99%

Sequential interactive image segmentation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…A so called human-in-the-loop approach has been investigated for different applications. Zhang et al (2020) investigate a human-in-the-loop approach for image segmentation and annotation. Schulz et al (2019) examine the use of suggestion models to support human experts with seg-mentation and classification of epistemic activities in diagnostic reasoning texts.…”

Section: Related Workmentioning

confidence: 99%

It is better to Verify: Semi-Supervised Learning with a human in the loop for large-scale NLU models

Weber¹,

Piovano²,

Bradford³

2021

Proceedings of the Second Workshop on Data Science With Human in the Loop: Language Advances

View full text Add to dashboard Cite

When a NLU model is updated, new utterances must be annotated to be included for training. However, manual annotation is very costly. We evaluate a semi-supervised learning workflow with a human in the loop in a production environment. The previous NLU model predicts the annotation of the new utterances, a human then reviews the predicted annotation. Only when the NLU prediction is assessed as incorrect the utterance is sent for human annotation. Experimental results show that the proposed workflow boosts the performance of the NLU model while significantly reducing the annotation volume. Specifically, in our setup, we see improvements of up to 14.16% for a recall-based metric and up to 9.57% for a F1score based metric, while reducing the annotation volume by 97% and overall cost by 60% for each iteration.

show abstract

Towards imbalanced motion: part-decoupling network for video portrait segmentation

Yu,

Xia,

2024

Sci. China Inf. Sci.

View full text Add to dashboard Cite

Human-in-the-loop image segmentation and annotation

Cited by 5 publications

References 9 publications

Sequential interactive image segmentation

Sequential interactive image segmentation

It is better to Verify: Semi-Supervised Learning with a human in the loop for large-scale NLU models

Towards imbalanced motion: part-decoupling network for video portrait segmentation

Contact Info

Product

Resources

About