Does Adding a Modality Really Make a Positive Impacts in Incomplete Multi-modal Brain Tumor Segmentation?

Previous incomplete multi-modal brain tumor segmentation technologies, while effective in integrating diverse modalities, commonly deliver under-expected performance gains. The reason lies in that the new modality may cause confused predictions due to uncertain and inconsistent patterns and quality in some positions, where the direct fusion consequently raises the negative gain for the final decision. In this paper, considering the potentially negative impacts within a modality, we propose multi-modal Positive-Negative impact region Double Calibration pipeline, called PNDC, to mitigate misinformation transfer of modality fusion.

Vertex points are not enough: Monocular 3D object detection via intra- and inter-plane constraints.

Existed methods for 3D object detection in monocular images focus mainly on the class of rigid bodies like cars, while more challenging detection like the cyclist is less studied. Therefore, we propose a novel 3D monocular object detection method to improve the accuracy of detection objects with large differences in deformation by introducing the geometric constraints of the object 3D bounding box plane. Considering the map relationship of projection plane and the keypoint, we firstly introduce the geometric constraints of the object 3D bounding box plane, adding the intra-plane constraint while regressing the position and offset of the keypoint itself, so that the position and offset error of the keypoint are always within the error range of the projection plane.