领域(数学)
一致性(知识库)
水下
领域(数学分析)
计算机科学
有机体
残余物
人工智能
相似性(几何)
透视图(图形)
光学(聚焦)
高斯分布
光场
连接(主束)
算法
分布(数学)
模式识别(心理学)
感知
计算机视觉
空格(标点符号)
物理
数据挖掘
生物系统
信号(编程语言)
领域分析
方案(数学)
数学
生物有机体
代表(政治)
作者
Tingkai Chen,Ning Wang
标识
DOI:10.1109/tcyb.2026.3667579
摘要
In this article, to exclusively conquer detection degradation of benthonic organisms due to domain shifting between training and testing scenarios, an innovative domain-adaptive detection scheme, termed DAD-ULC, is holistically invented by uniformising light field and color distribution. To that end, the encoder-decoder domain converter (EDDC) with residual connection is created, such that samples in degraded domains can be transformed into a unified domain. The underwater light field perception loss (ULFPL) is further conceptualized by virtue of a multiscale Gaussian filter, so as to directly expedite light-field conversion, getting rid of benthonic organism structure information, thereby facilitating light-domain adaptation. By exploiting the similarity between generated and referenced images in Lab space, a color distribution consistency loss (CDCL) is empowered for color-distribution transfer. Eventually, the DAD-ULC scheme is established in an end-to-end manner by integrating with EDDC, ULFPL, and CDCL modules, thereby enabling identical light-color domains between training and testing samples. Comprehensive experiments and comparisons conducted on detecting underwater objects (DUOs) and URPC2020 datasets sufficiently demonstrate effectiveness and superiority in diversified domain-shifting challenges.
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