aigc数字人驱动-香港中文大学-AIGC驱动的3D场景理解及医学图像解析(附下载)

今天分享的是人工智能AI系列深度研究报告：《香港中文大学-AIGC驱动的3D场景理解及医学图像解析》精选报告来源公众号：【人工智能学派】aigc数字人驱动，回复关键字“6688”，获取完整PDF电子版

研究报告内容摘要如下

Cons:

1. The object proposals in the large 3D scene are usually redundant;2.The appearance and attribute information is not sufficiently captured;3.The relations among proposals and the ones between proposals and backgroundare not fully studied

ScanRefer generates 114 possible candidates after filteringproposals by their objectness scores,Each proposal 's feature is generated by the detection frameworkThere is no relation reasoning among proposals

InstanceRefer Architecture:

Scoring each candidate matching language and visual features (the candidatewith the largest score will be regarded as output).

It construct a four-layer Sparse ConvolutionSparseConv) as the feature extractor;After an average pooling, the global attributeperception feature is obtained.

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本报告共计：63页aigc数字人驱动。受篇幅限制，仅展示部分内容。

精选报告来源公众号：【人工智能学派】aigc数字人驱动，回复关键字“6688”，获取完整PDF电子版