Summary of Llm-hdr: Bridging Llm-based Perception and Self-supervision For Unpaired Ldr-to-hdr Image Reconstruction, by Hrishav Bakul Barua et al.
LLM-HDR: Bridging LLM-based Perception and Self-Supervision for Unpaired LDR-to-HDR Image Reconstruction
by Hrishav Bakul Barua, Kalin Stefanov, Lemuel Lai En Che, Abhinav Dhall, KokSheik Wong, Ganesh Krishnasamy
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes LLM-HDR, a method that integrates the perception of Large Language Models (LLMs) into a modified semantic- and cycle-consistent adversarial architecture for translating Low Dynamic Range (LDR) to High Dynamic Range (HDR) images. The proposed approach utilizes unpaired {LDR,HDR} datasets for training, addressing the limited literature on using such datasets for this task. LLM-HDR introduces novel artifact- and exposure-aware generators to remove visual artifacts and an encoder and loss to ensure semantic consistency. This self-supervised method achieves state-of-the-art performance across several benchmark datasets and reconstructs high-quality HDR images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make High Dynamic Range (HDR) pictures from Low Dynamic Range (LDR) ones using Large Language Models. The model doesn’t need paired LDR-HDR data like most current methods do, which makes it more useful for real-world applications. The approach uses unpaired datasets and has special features to remove bad parts of the image and keep the correct colors. This method is the first to use a language model for this task and does very well on different benchmark tests. |
Keywords
* Artificial intelligence * Encoder * Language model * Self supervised
