Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Zhao, Danpei"'
Significant advancements have been made in semantic image synthesis in remote sensing. However, existing methods still face formidable challenges in balancing semantic controllability and diversity. In this paper, we present a Hybrid Semantic Embeddi
Externí odkaz:
http://arxiv.org/abs/2411.14781
Current methods for disaster scene interpretation in remote sensing images (RSIs) mostly focus on isolated tasks such as segmentation, detection, or visual question-answering (VQA). However, current interpretation methods often fail at tasks that req
Externí odkaz:
http://arxiv.org/abs/2410.13384
Improving search efficiency serves as one of the crucial objectives of Neural Architecture Search (NAS). However, many current approaches ignore the universality of the search strategy and fail to reduce the computational redundancy during the search
Externí odkaz:
http://arxiv.org/abs/2408.01311
Continual semantic segmentation (CSS) based on incremental learning (IL) is a great endeavour in developing human-like segmentation models. However, current CSS approaches encounter challenges in the trade-off between preserving old knowledge and lea
Externí odkaz:
http://arxiv.org/abs/2407.15429
Continual learning (CL) breaks off the one-way training manner and enables a model to adapt to new data, semantics and tasks continuously. However, current CL methods mainly focus on single tasks. Besides, CL models are plagued by catastrophic forget
Externí odkaz:
http://arxiv.org/abs/2407.14242
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing, 2024
Current remote-sensing interpretation models often focus on a single task such as detection, segmentation, or caption. However, the task-specific designed models are unattainable to achieve the comprehensive multi-level interpretation of images. The
Externí odkaz:
http://arxiv.org/abs/2404.04608
Fine-grained object detection (FGOD) extends object detection with the capability of fine-grained recognition. In recent two-stage FGOD methods, the region proposal serves as a crucial link between detection and fine-grained recognition. However, cur
Externí odkaz:
http://arxiv.org/abs/2312.10515
Autor:
Yuan, Bo, Zhao, Danpei
Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. It breaks through the obstacle of one-way training on close sets and enables continuous adaptive learning on open-s
Externí odkaz:
http://arxiv.org/abs/2310.14277
Publikováno v:
IEEE TPAMI 2023
As a front-burner problem in incremental learning, class incremental semantic segmentation (CISS) is plagued by catastrophic forgetting and semantic drift. Although recent methods have utilized knowledge distillation to transfer knowledge from the ol
Externí odkaz:
http://arxiv.org/abs/2309.15413
Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. Present UDA models focus on alleviating the domain shift by minimizing the feature discrepancy between the
Externí odkaz:
http://arxiv.org/abs/2204.02111