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cs.CV2026
MGRL-RSCC: Multi-Granularity Reward Reinforcement Learning for Fine-Grained Remote Sensing Change Captioning
Futian Wang, Mengqi Wang, Xiao Wang +4
Remote Sensing Change Captioning (RSCC), which aims to generate accurate and detailed linguistic descriptions of ground object variations from bi-temporal remote sensing images, is…
cs.CV2026
Parameter-Dynamic Adaptive Fusion and Calibration Network for RGBT Tracking
Zhaoding Ding, Chenglong Li, Jiandong Jin +2
Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibil…
cs.CV2026
T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval
Xiao Wang, Ziwen Wang, Weizhe Kong +5
Vehicle Re-identification (Re-ID) aims to retrieve the most similar image to a given query from images captured by non-overlapping cameras. Extending vehicle Re-ID from image-only…