This survey provides a comprehensive evaluation of various deep learning-based segmentation architectures, covering a wide range of models, from traditional ones like FCN and PSPNet to more modern approaches like SegFormer and FAN, and proposes to evaluate the methods in terms of temporal consistency and corruption vulnerability.
A novel multi-modal segmentation framework based on a cross-attention mechanism was presented that enables more effective interaction between RGB and depth features and shows consistent improvements in segmentation accuracy, particularly in challenging scenarios.
Noor Safa, Zainab Majeed Abid· Academic Journal of Electric...· 0 citations
This work proposes albedo and shading gradient descriptors which are derived from physics-based models and is the first to directly address the texture and intensity ambiguity problems of the shading estimations.
A. S. Baslamisli, Yang Liu, Sezer Karaoglu et al.· 0 citations
A novel IIS framework based on Multi-Layer Perceptron (MLP), which fuses clicks from users with feature representations extracted from the Transformer Encoder, which closes the gap between interactive user instructions and deep learning and provides a practical and flexible solution for accurate segmentation in many ap...
S. Raghavendra, Rithika Shyam Kumar, S. Abhilash et al.· IEEE Access· 0 citations
FSANet is proposed, which integrates prior knowledge with a dual-domain solver to sequentially adapt to diverse segmentation tasks and introduces SceneX, a novel open-source dataset featuring 10 challenging non-ideal scenarios, establishing a new benchmark for evaluating and improving the robustness and real-world appl...
Rui-Bo Wang, Zi-Yi Shen, Hua-Ming Wu et al.· 0 citations
Street View Images (SVI) are high-resolution, geo-referenced panoramas that capture real-world environments. Integration of Artificial Intelligence (AI) with SVI enables automated analysis for a range of urban applications including object detection, semantic segmentation, text recognition, scene understanding, and soc...
Ranjani A, J. C, V. V et al.· 2026 7th International Confe...· 0 citations
This paper presents a comparative study of segmentation architectures, ranging from convolutional backbones to vision transformers, applied to the B.O.V.I.D. dataset, a corpus of high-resolution bovid dental photographs paired with hand-made segmentation masks not originally designed for ML-based training, and evaluate...
Keith G. Mills, Evan B. Sanders, Gregory J. Matthews et al.· 0 citations
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