Floating Waste Discovery by Request via Object-Centric Learning
作者机构:School of Computer ScienceFudan UniversityShanghai200438China
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2024年第80卷第7期
页 面:1407-1424页
核心收录:
学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:Fudan University
主 题:Unsupervised object discovery object-centric learning pseudo data generation real-world object discovery by request
摘 要:Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental ***,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data ***,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable *** solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic *** proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request ***-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene *** network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the *** the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test *** have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.