PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.
Changes to captions: An attentive network for remote sensing change captioning.IEEE Transactions on Image Processing, 32:6047–6060
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SCPT encodes pairwise class relationships via signed random projection and applies SVD-based semantic denoising to improve CLIP prompt tuning for fine-grained image recognition.
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UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model
PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.
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Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition
SCPT encodes pairwise class relationships via signed random projection and applies SVD-based semantic denoising to improve CLIP prompt tuning for fine-grained image recognition.