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Found 7 papers, 2 papers with code
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion
To effectively mine the complementary information and adapt to misalignment situations, we propose a Multispectral Deformable Cross-attention module to adaptively sample and aggregate multi-semantic level features of infrared and visible images for each object.
InfMAE: A Foundation Model in Infrared Modality
Finally, based on the fact that infrared images do not have a lot of details and texture information, we design an infrared decoder module, which further improves the performance of downstream tasks.
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations.
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud
In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.
Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range.
Local Patch Network with Global Attention for Infrared Small Target Detection
A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.
