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Found 6 papers, 2 papers with code
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation
We introduce a novel training strategy for stereo matching and optical flow estimation that utilizes image-to-image translation between synthetic and real image domains.
DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation
Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images.
QuadFormer: Quadruple Transformer for Unsupervised Domain Adaptation in Power Line Segmentation of Aerial Images
This process is studied in Unsupervised domain adaptation (UDA).
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes
In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes.
Ranked #2 on
Long-tail Learning
on CIFAR-100-LT (ρ=200)
