33 citations · 82 across the 17 of their papers we have counts for
6 papers · 1 filter
Transformer-based Image Compression with Variable Image Quality Objectives
Chia-Hao Kao, Yi-Hsin Chen, Cheng Chien +2
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 f…
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang +2
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokeniz…
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang +2
Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the r…
TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception
Yi-Hsin Chen, Ying-Chieh Weng, Chia-Hao Kao +3
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 Tra…
Transformer-based Variable-rate Image Compression with Region-of-interest Control
Chia-Hao Kao, Ying-Chieh Weng, Yi-Hsin Chen +2
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-in…
Multimodal Prompting with Missing Modalities for Visual Recognition
Yi-Lun Lee, Yi-Hsuan Tsai, Wei-Chen Chiu +1
In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; a…