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.
Image segmentation remains a challenging task, particularly in complex environments where visual information from RGB images alone is often insufficient. Factors such as poor lighting, occlusions, and background clutter can significantly degrade segmentation performance. To address these limitations, multi-modal approa...
Noor Safa, Zainab Majeed Abid· Academic Journal of Electric...· 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
Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial features, leading to high computational costs and neglecting prior knowledge or noise patterns, resulting in missing detail...
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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