Discriminative feature fusion for RGB-D salient object detection

Chen, Zeyu; Zhu, Mingyu; Chen, Shuhan; Lu, Lu; Tang, Haotian; Hu, Xuelong; Ji, Chunfan

doi:10.1016/j.compeleceng.2023.108579

Cited by 1 publication

(1 citation statement)

References 49 publications

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“…Chen et al addressed challenges in RGB-D salient object detection. They introduced a unified feature fusion module, enhancing both semantic and spatial information, and a multi-scale contextual perception module [148]. Their method, outperforming 14 state-of-the-art approaches on eight datasets, effectively combines RGB and depth features.…”

Section: B Depth Perceptionmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

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Section: B Depth Perceptionmentioning

confidence: 99%