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|a 10.1109/TPAMI.2025.3573609
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|a eng
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| 100 |
1 |
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|a Zhu, Lanyun
|e verfasserin
|4 aut
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| 245 |
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|a LLaFS++
|b Few-Shot Image Segmentation With Large Language Models
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|c 2025
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|a Date Revised 07.08.2025
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Despite the rapid advancements in few-shot segmentation (FSS), most of existing methods in this domain are hampered by their reliance on the limited and biased information from only a small number of labeled samples. This limitation inherently restricts their capability to achieve sufficiently high levels of performance. To address this issue, this paper proposes a pioneering framework named LLaFS++, which, for the first time, applies large language models (LLMs) into FSS and achieves notable success. LLaFS++ leverages the extensive prior knowledge embedded by LLMs to guide the segmentation process, effectively compensating for the limited information contained in the few-shot labeled samples and thereby achieving superior results. To enhance the effectiveness of the text-based LLMs in FSS scenarios, we present several innovative and task-specific designs within the LLaFS++ framework. Specifically, we introduce an input instruction that allows the LLM to directly produce segmentation results represented as polygons, and propose a region-attribute corresponding table to simulate the human visual system and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization, and propose a novel inference method to mitigate potential oversegmentation hallucinations caused by the regional guidance information. Incorporating these designs, LLaFS++ constitutes an effective framework that achieves state-of-the-art results on multiple datasets including PASCAL-$5^{i}$5i, COCO-$20^{i}$20i, and FSS-1000. Our superior performance showcases the remarkable potential of applying LLMs to process few-shot vision tasks
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Chen, Tianrun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Deyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ye, Jieping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Jun
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 9 vom: 26. Aug., Seite 7715-7732
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
1 |
8 |
|g volume:47
|g year:2025
|g number:9
|g day:26
|g month:08
|g pages:7715-7732
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2025.3573609
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