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.
Semantic-CC: Boosting remote sensing image change cap- 18 Y
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.
citing papers explorer
-
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.
-
RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.