PVPUFormer : Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation

Integration of diverse visual prompts like clicks, scribbles, and boxes in interactive image segmentation significantly facilitates users' interaction as well as improves interaction efficiency. However, existing studies primarily encode the position or pixel regions of prompts without consider...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - PP(2024) vom: 12. Nov.
1. Verfasser: Zhang, Xu (VerfasserIn)
Weitere Verfasser: Yang, Kailun, Lin, Jiacheng, Yuan, Jin, Li, Zhiyong, Li, Shutao
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
LEADER 01000naa a22002652 4500
001 NLM380180626
003 DE-627
005 20241115235043.0
007 cr uuu---uuuuu
008 241115s2024 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2024.3492713  |2 doi 
028 5 2 |a pubmed24n1601.xml 
035 |a (DE-627)NLM380180626 
035 |a (NLM)39531563 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Zhang, Xu  |e verfasserin  |4 aut 
245 1 0 |a PVPUFormer  |b Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation 
264 1 |c 2024 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 12.11.2024 
500 |a published: Print-Electronic 
500 |a Citation Status Publisher 
520 |a Integration of diverse visual prompts like clicks, scribbles, and boxes in interactive image segmentation significantly facilitates users' interaction as well as improves interaction efficiency. However, existing studies primarily encode the position or pixel regions of prompts without considering the contextual areas around them, resulting in insufficient prompt feedback, which is not conducive to performance acceleration. To tackle this problem, this paper proposes a simple yet effective Probabilistic Visual Prompt Unified Transformer (PVPUFormer) for interactive image segmentation, which allows users to flexibly input diverse visual prompts with the probabilistic prompt encoding and feature post-processing to excavate sufficient and robust prompt features for performance boosting. Specifically, we first propose a Probabilistic Prompt-unified Encoder (PPuE) to generate a unified one-dimensional vector by exploring both prompt and non-prompt contextual information, offering richer feedback cues to accelerate performance improvement. On this basis, we further present a Prompt-to-Pixel Contrastive (P2C) loss to accurately align both prompt and pixel features, bridging the representation gap between them to offer consistent feature representations for mask prediction. Moreover, our approach designs a Dual-cross Merging Attention (DMA) module to implement bidirectional feature interaction between image and prompt features, generating notable features for performance improvement. A comprehensive variety of experiments on several challenging datasets demonstrates that the proposed components achieve consistent improvements, yielding state-of-the-art interactive segmentation performance. Our code is available at https://github.com/XuZhang1211/PVPUFormer 
650 4 |a Journal Article 
700 1 |a Yang, Kailun  |e verfasserin  |4 aut 
700 1 |a Lin, Jiacheng  |e verfasserin  |4 aut 
700 1 |a Yuan, Jin  |e verfasserin  |4 aut 
700 1 |a Li, Zhiyong  |e verfasserin  |4 aut 
700 1 |a Li, Shutao  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g PP(2024) vom: 12. Nov.  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:PP  |g year:2024  |g day:12  |g month:11 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2024.3492713  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d PP  |j 2024  |b 12  |c 11