Timing Yang (@TimingYang99) profile picture

PhD @ JHU | MS USC | Prev. Intern at Mayo, Tsinghua IIIS | Computer Vision, Robotics & Medical AI

Joined March 2026
Photo by Timing Yang (@TimingYang99)
8. Let RGB Be the Language of Vision ๐Ÿ”‘ Keywords: RGB In and RGB Out (RINO), unified vision-language systems, structured visual signals, zero-shot performance ๐Ÿ’ก Category: Computer Vision ๐ŸŒŸ Research Objective: - This research introduces a unified formulation for vision models, termed as RGB In and RGB Out (RINO), to handle diverse visual information as RGB images and convert visual tasks into a common RGB-to-RGB image editing problem. ๐Ÿ› ๏ธ Research Methods: - The study utilizes a generic image editing backbone without task-specific fine-tuning, allowing diverse visual tasks to share encoding and decoding architecture, similar to text operation in language models. ๐Ÿ’ฌ Research Conclusions: - RINO displays robust zero-shot performance in both dense understanding tasks like segmentation and dense-conditioned generation tasks like pose-to-image generation, advancing towards creating general unified vision-language systems. ๐Ÿ‘‰ Paper link: huggingface.co/papers/2607.1โ€ฆ
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Timing Yang retweeted
Two months ago, I vaguely posted a number: 0.9 FID, one-step, pixel space. Now it is 0.75, and can be even lower. Many wonder how. I thought it might end as a small FID prank: simple and deliberate. It started with one question: can FID be optimized directly, and what does it reveal? Introducing FD-loss.
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