Search Results for author: Heming Sun

Found 17 papers, 5 papers with code

ABCAS: Adaptive Bound Control of spectral norm as Automatic Stabilizer

no code implementations12 Nov 2022 Shota Hirose, Shiori Maki, Naoki Wada, Heming Sun, Jiro Katto

Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network.

Semantic Segmentation in Learned Compressed Domain

no code implementations3 Sep 2022 Jinming Liu, Heming Sun, Jiro Katto

Most machine vision tasks (e. g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e. g., JPEG).

Image Compression Semantic Segmentation

Streaming-capable High-performance Architecture of Learned Image Compression Codecs

no code implementations2 Aug 2022 Fangzheng Lin, Heming Sun, Jiro Katto

Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage.

Image Compression

Memory-Efficient Learned Image Compression with Pruned Hyperprior Module

no code implementations21 Jun 2022 Ao Luo, Heming Sun, Jinming Liu, Jiro Katto

Learned Image Compression (LIC) gradually became more and more famous in these years.

Image Compression

Q-LIC: Quantizing Learned Image Compression with Channel Splitting

no code implementations28 May 2022 Heming Sun, Lu Yu, Jiro Katto

Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra.

Image Compression MS-SSIM +2

End-to-End Learned Image Compression with Quantized Weights and Activations

no code implementations17 Nov 2021 Heming Sun, Lu Yu, Jiro Katto

To our best knowledge, this is the first work to give a complete analysis on the coding gain and the memory cost for a quantized LIC network, which validates the feasibility of the hardware implementation.

Image Compression MS-SSIM +2

Learned Image Compression with Separate Hyperprior Decoders

no code implementations31 Oct 2021 Zhao Zan, Chao Liu, Heming Sun, Xiaoyang Zeng, Yibo Fan

Learned image compression techniques have achieved considerable development in recent years.

Image Compression MS-SSIM +1

Learned Video Compression with Residual Prediction and Loop Filter

1 code implementation19 Aug 2021 Chao Liu, Heming Sun, Jiro Katto, Xiaoyang Zeng, Yibo Fan

To reduce the complexity, a light ResNet structure is used as the backbone for both RP-Net and LF-Net.

Motion Compensation Video Compression

Fully Neural Network Mode Based Intra Prediction of Variable Block Size

1 code implementation5 Aug 2021 Heming Sun, Lu Yu, Jiro Katto

As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

regression

A QP-adaptive Mechanism for CNN-based Filter in Video Coding

no code implementations25 Oct 2020 Chao Liu, Heming Sun, Jiro Katto, Xiaoyang Zeng, Yibo Fan

Convolutional neural network (CNN)-based filters have achieved great success in video coding.

Quantization

COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

1 code implementation EMNLP 2021 Xinliang Frederick Zhang, Heming Sun, Xiang Yue, Simon Lin, Huan Sun

For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1, 236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.

Retrieval

Dual Learning-based Video Coding with Inception Dense Blocks

no code implementations22 Nov 2019 Chao Liu, Heming Sun, Junan Chen, Zhengxue Cheng, Masaru Takeuchi, Jiro Katto, Xiaoyang Zeng, Yibo Fan

This method is mainly composed of two parts: intra prediction and reconstruction filtering.

Learning Image and Video Compression through Spatial-Temporal Energy Compaction

no code implementations CVPR 2019 Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto

Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach.

Image Compression MS-SSIM +2

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