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pro vyhledávání: '"Huang, Peixiang"'
Pathological diagnosis remains the definitive standard for identifying tumors. The rise of multimodal large models has simplified the process of integrating image analysis with textual descriptions. Despite this advancement, the substantial costs ass
Externí odkaz:
http://arxiv.org/abs/2408.07037
Autor:
Yang, Cheng, Xu, Rui, Guo, Ye, Huang, Peixiang, Chen, Yiru, Ding, Wenkui, Wang, Zhongyuan, Zhou, Hong
Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have been developed and promoted th
Externí odkaz:
http://arxiv.org/abs/2311.05298
Deep Neural Networks (DNNs) are expected to provide explanation for users to understand their black-box predictions. Saliency map is a common form of explanation illustrating the heatmap of feature attributions, but it suffers from noise in distingui
Externí odkaz:
http://arxiv.org/abs/2311.05143
Autor:
Huang, Peixiang, Zhang, Songtao, Gan, Yulu, Xu, Rui, Zhu, Rongqi, Qin, Wenkang, Guo, Limei, Jiang, Shan, Luo, Lin
Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various im
Externí odkaz:
http://arxiv.org/abs/2310.20427
Pathological captioning of Whole Slide Images (WSIs), though is essential in computer-aided pathological diagnosis, has rarely been studied due to the limitations in datasets and model training efficacy. In this paper, we propose a new paradigm Subty
Externí odkaz:
http://arxiv.org/abs/2310.20607
Autor:
Pan, Mingjie, Liu, Jiaming, Zhang, Renrui, Huang, Peixiang, Li, Xiaoqi, Wang, Bing, Xie, Hongwei, Liu, Li, Zhang, Shanghang
3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels. Recent works mainly utilize complete occupancy labels in 3D voxel space for s
Externí odkaz:
http://arxiv.org/abs/2309.09502
Autor:
Pan, Mingjie, Liu, Li, Liu, Jiaming, Huang, Peixiang, Wang, Longlong, Zhang, Shanghang, Xu, Shaoqing, Lai, Zhiyi, Yang, Kuiyuan
In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023. Existing methods for occupancy prediction primarily focus on optimizing project
Externí odkaz:
http://arxiv.org/abs/2306.09117
To achieve accurate and low-cost 3D object detection, existing methods propose to benefit camera-based multi-view detectors with spatial cues provided by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV) feature distillation.
Externí odkaz:
http://arxiv.org/abs/2212.13979
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