Zobrazeno 1 - 10
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pro vyhledávání: '"Chen, Yi‐Hsin"'
This paper aims to delve into the rate-distortion-complexity trade-offs of modern neural video coding. Recent years have witnessed much research effort being focused on exploring the full potential of neural video coding. Conditional autoencoders hav
Externí odkaz:
http://arxiv.org/abs/2410.03898
Autor:
Kao, Chia-Hao, Chien, Cheng, Tseng, Yu-Jen, Chen, Yi-Hsin, Gnutti, Alessandro, Lo, Shao-Yuan, Peng, Wen-Hsiao, Leonardi, Riccardo
This paper presents the first-ever study of adapting compressed image latents to suit the needs of downstream vision tasks that adopt Multimodal Large Language Models (MLLMs). MLLMs have extended the success of large language models to modalities (e.
Externí odkaz:
http://arxiv.org/abs/2407.19651
Autor:
Chen, Yi-Hsin, Ho, Kuan-Wei, Tsai, Shiau-Rung, Lin, Guan-Hsun, Gnutti, Alessandro, Peng, Wen-Hsiao, Leonardi, Riccardo
This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from a single compressed bitstream. Instead of training separate decoders f
Externí odkaz:
http://arxiv.org/abs/2402.12888
Rydberg-assisted atomic electrometry with thermal vapors offers a promising approach for detecting external electric fields. This technique, however, has proven quite challenging for measuring low frequencies because of the effects of metal-alkali at
Externí odkaz:
http://arxiv.org/abs/2402.01430
Autor:
Gnutti, Alessandro, Della Fiore, Stefano, Savardi, Mattia, Chen, Yi-Hsin, Leonardi, Riccardo, Peng, Wen-Hsiao
The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction
Externí odkaz:
http://arxiv.org/abs/2401.06517
Autor:
Chen, Yi-Hsin, Xie, Hong-Sheng, Chen, Cheng-Wei, Gao, Zong-Lin, Benjak, Martin, Peng, Wen-Hsiao, Ostermann, Jörn
Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus p
Externí odkaz:
http://arxiv.org/abs/2312.15829
Publikováno v:
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with var
Externí odkaz:
http://arxiv.org/abs/2309.12717
This work addresses continuous space-time video super-resolution (C-STVSR) that aims to up-scale an input video both spatially and temporally by any scaling factors. One key challenge of C-STVSR is to propagate information temporally among the input
Externí odkaz:
http://arxiv.org/abs/2307.07988
Autor:
Chen, Yi-Hsin, Weng, Ying-Chieh, Kao, Chia-Hao, Chien, Cheng, Chiu, Wei-Chen, Peng, Wen-Hsiao
This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired
Externí odkaz:
http://arxiv.org/abs/2306.05085
This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-interest (ROI) functionality. Inspired by prompt tuning, we introduce pr
Externí odkaz:
http://arxiv.org/abs/2305.10807