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This paper analyzes various decoder architectures used in machine vision tasks that require pixel-wise predictions, such as semantic segmentation and depth estimation. It highlights the importance of decoders, introduces new techniques like residual-like connections and bilinear additive upsampling, and presents a comprehensive comparison of different decoding methods across multiple tasks. The findings indicate significant performance variance based on decoder choice, emphasizing the need for further resea

Author

SASWATA MAITY

Language

EN