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Found 4 papers, 1 papers with code
Semantic-aware Network for Aerial-to-Ground Image Synthesis
Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image.
Probabilistic Prompt Learning for Dense Prediction
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models.
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity
In this paper, we present a novel spatially-adaptive self-similarity (SASS) for unsupervised asymmetric stereo matching.
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection
By learning sampling offset to the grid of standard convolution, the network can robustly extract the features from targets with shape variations for SAR ship detection.
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