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A Holistically-Guided Decoder for Deep Representation Learning With Applications to Semantic Segmentation and Object

Jianbo Liu, Junjun He, Yuanjie Zheng

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    This study introduces a holistically-guided decoder for efficient semantic segmentation and object detection. It achieves comparable or better performance than state-of-the-art methods with significantly reduced computational costs.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Segmentation
    • Object Detection

    Background:

    • High-level and high-resolution feature representations are crucial for visual understanding tasks.
    • Dilated convolutions offer high-resolution semantic maps but incur high computational and memory costs.
    • Existing encoder-decoder methods are efficient but lag in performance compared to dilated convolution approaches for semantic segmentation.

    Purpose of the Study:

    • To develop a novel holistically-guided decoder to generate high-resolution, semantic-rich feature maps.
    • To balance performance and efficiency in visual understanding tasks, particularly semantic segmentation and object detection.
    • To improve the performance of encoder-decoder architectures for semantic segmentation.

    Main Methods:

    • Proposed a novel holistically-guided decoder utilizing holistic codeword generation and assembly operations.
    • Leveraged multi-scale encoder features, combining high-level and low-level information.
    • Implemented the decoder in EfficientFCN for semantic segmentation and HGD-FPN for object detection and instance segmentation.

    Main Results:

    • EfficientFCN achieved comparable or superior performance to state-of-the-art methods for semantic segmentation on PASCAL Context, PASCAL VOC, and ADE20K datasets.
    • EfficientFCN demonstrated significantly reduced computational costs (1/3) compared to existing methods.
    • HGD-FPN integrated into object detection frameworks (ResNet-50 backbone) achieved higher mean Average Precision (mAP).

    Conclusions:

    • The holistically-guided decoder effectively generates high-resolution, semantic-rich feature maps.
    • The proposed approach offers a significant improvement in efficiency without compromising, and often enhancing, performance.
    • This method provides a promising direction for developing more efficient and effective deep learning models for visual understanding tasks.