Learn A Compression for Objection Detection - VAE with a Bridge

Mei, Yixin; Li, Fan; Li, Li; Li, Zhu

doi:10.1109/vcip53242.2021.9675387

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Since deep features can be larger than the original image size, [26] and [27] use an auto-encoder(AE) to reduce the feature dimension and size. Similarly, [28] introduces a Variational Auto-Encoder (VAE) model to reduce feature size while preserving task performance.…”

Section: B Deep Feature Compression With Leaning-based Methodsmentioning

confidence: 99%

Deep Feature Compression with Collaborative Coding of Image Texture

Xiong

Liu

Zhu

et al. 2022

2022 IEEE International Symposium on Circuits and Systems (ISCAS)

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With the rapid development of machine vision technology in recent years, many researchers have begun to focus on feature compression that is better suited for machine vision tasks. The target of feature compression is deep features, which arise from convolution in the middle layer of a pre-trained convolutional neural network. However, due to the large volume of data and high level of abstraction of deep features, their application is primarily limited to machine-centric scenarios, which poses significant constraints in situations requiring humancomputer interaction. This paper investigates features and textures and proposes a texture-guided feature compression strategy based on their characteristics. Specifically, the strategy comprises feature layers and texture layers. The feature layers serve the machine, including a feature selection module and a feature reconstruction network. With the assistance of texture images, they selectively compress and transmit channels relevant to visual tasks, reducing feature data while providing high-quality features for the machine. The texture layers primarily serve humans and consist of an image reconstruction network. This image reconstruction network leverages features and texture images to reconstruct preview images for humans. Our method fully exploits the characteristics of texture and features. It eliminates feature redundancy, reconstructs high-quality preview images for humans, and supports decision-making. The experimental results demonstrate excellent performance when employing our proposed method to compress the deep features.

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Section: B Deep Feature Compression With Leaning-based Methodsmentioning

confidence: 99%

Deep Feature Compression with Collaborative Coding of Image Texture

Xiong

Liu

Zhu

et al. 2022

2022 IEEE International Symposium on Circuits and Systems (ISCAS)

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show abstract

“…This allows only task-relevant information to be encoded, resulting in high task accuracy and compression rates. For example, studies such as [30,31] have used classification and semantic segmentation loss functions to optimize the decoder for task accuracy that is similar to the original image.…”

Section: Dic For Single Contextmentioning

confidence: 99%

Conditional Encoder-Based Adaptive Deep Image Compression with Classification-Driven Semantic Awareness

et al. 2023

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This paper proposes a new algorithm for adaptive deep image compression (DIC) that can compress images for different purposes or contexts at different rates. The algorithm can compress images with semantic awareness, which means classification-related semantic features are better protected in lossy image compression. It builds on the existing conditional encoder-based DIC method and adds two features: a model-based rate-distortion-classification-perception (RDCP) framework to control the trade-off between rate and performance for different contexts, and a mechanism to generate coding conditions based on image complexity and semantic importance. The algorithm outperforms the QMAP2021 benchmark on the ImageNet dataset. Over the tested rate range, it improves the classification accuracy by 11% and the perceptual quality by 12.4%, 32%, and 1.3% on average for NIQE, LPIPS, and FSIM metrics, respectively.

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